Structural Health Monitoring

REF

2010-01-09T18:29:00.000-08:00

1. Recent enhancements to the SmartBrick structural health monitoring platform
Harms, Tyler (Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, United States); Bastianini, Filippo; Sedigh, Sahra Source: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, p 358-363, 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
2. Wireless sensor networks in structural health monitoring based on ZigBee technology
Jiang, Xiang-Dong (Dept. of Communication Engineering, Xiamen University, Xiamen, Fujian, China); Tang, Yu-Liang; Lei, Ying Source: 2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, ASID 2009, 2009, 2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, ASID 2009
3. Autonomous decentralized structural health monitoring using smart sensors
Nagayama, T. (Department of Civil Engineering, University of Tokyo, 7-3-1, Hongo, Bunkyo-ku Tokyo 113-8656, Japan); Spencer Jr., B.F.; Rice, Jennifer A. Source: Structural Control and Health Monitoring, v 16, n 7-8, p 842-859, November-December 2009, Takuji Kobori Memorial Issue
4. High resolution, dynamic strain measurements with continuous fiber Bragg gratings for structural health monitoring
Sang, Alex (Luna Innovations Incorporated, 1 Riverside circle, Roanoke, VA 24016, United States); Marsden, Kevin; Wolfe, Matt; Gifford, Dawn; Froggatt, Mark; Soller, Brian Source: 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09, p 29-31, 2009, 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09
5. Structural health monitoring and structural identification for long-span bridges
Grimmelsman, Kirk A. (Department of Civil Engineering, University of Arkansas, Fayetteville, United States) Source: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, p 364-369, 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
6. Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model
Dong, Yinfeng (College of Civil Engineering, Chongqing University, Chongqing, 400045, China); Li, Yingmin; Lai, Ming Source: Soil Dynamics and Earthquake Engineering, v 30, n 3, p 133-145, March 2010
7. Methodology and results of the investigation monitoring in the Russian NPP vicinity
Shandala, N.K. (Burnasyan Federal Medical Biophysical Centre, Russia); Novikova, N.Ya.; Kiselev, S.M.; Isaev, D.V.; Seregin, V.A.; Metlyaev, E.G.; Shchagin, Yu.A. Source: WIT Transactions on Biomedicine and Health, v 14, p 371-379, 2009, Environmental Health Risk V
8. Performance of equilibrium-based system identification algorithms with incomplete state data
Elmer, W. (Civil and Env. Eng. Department, University of California, Los Angeles, CA 90095, United States); Stafsudd, J.Z.; Taciroglu, E. Source: Engineering Structures, v 32, n 2, p 483-497, February 2010
9. A comparison of approaches to multivariate extreme value theory for novelty detection
Clifton, David A. (Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Roosevelt Drive, Oxford, OX3 7DQ, United Kingdom); Hugueny, Samuel; Tarassenko, Lionel Source: IEEE Workshop on Statistical Signal Processing Proceedings, p 13-16, 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09
10. Structural identification using a low-cost search method
Fonda, James W. (University of Missouri-Rolla, Rolla, MO 65409, United States); Watkins, Steve E. Source: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, p 383-387, 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
11. Design of user interface in portable health monitoring system
Liu, Weifeng (Department of Biomedical Engineering, Beijing Institute of Technology, Beijing, China); Dai, Zhiling; Hu, Xinfen; Tang, Xiaoying Source: 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, 2009, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
12. Structural Control and Health Monitoring: Preface
Fujino, Yozo; Nishitani, Akira Source: Structural Control and Health Monitoring, v 16, n 7-8, p 687-688, November-December 2009, Takuji Kobori Memorial Issue
13. Integrated design of inelastic controlled structural systems
Cimellaro, G.P. (Department of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, NY 14260, United States); Soong, T.T.; Reinhorn, A.M. Source: Structural Control and Health Monitoring, v 16, n 7-8, p 689-702, November-December 2009, Takuji Kobori Memorial Issue
14. Active and semi-active vibration control of buildings in Japan-practical applications and verification
Ikeda, Yoshiki (Kobori Research Complex, Kajima Corporation, KI Building 6-5-30 Akasaka, Minato-ku, Tokyo, Japan) Source: Structural Control and Health Monitoring, v 16, n 7-8, p 703-723, November-December 2009, Takuji Kobori Memorial Issue
15. High temperature fiber optic pressure sensors for engine dynamics and health monitoring
Gahan, David (Oxsensis Ltd., Oxford, United Kingdom); Fasham, Stephen; Harpin, Arnold Source: 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09, p 23-24, 2009, 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09
16. Landscape assessment of a stable aspen community in southern Utah, USA
Rogers, Paul C. (Western Aspen Alliance and Ecology Center, Utah State University, Department of Wildland Resources, 5230 Old Main Hill, Logan, UT 84322, United States); Leffler, A. Joshua; Ryel, Ronald J. Source: Forest Ecology and Management, v 259, n 3, p 487-495, January 25, 2010
17. Hyperbolic control for vibration mitigation of a base-isolated benchmark structure
Pozo, Francesc (Departament De Matemàtica Aplicada III, Escola Universitària, CoDAlab, Comte d'Urgell, 187, 08036 Barcelona, Spain); Acho, Leonardo; Rodellar, José Source: Structural Control and Health Monitoring, v 16, n 7-8, p 766-783, November-December 2009, Takuji Kobori Memorial Issue
18. 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
Source: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
19. A passive control device with SMA components: From the prototype to the model
Casciati, Fabio (Department of Structural Mechanics, University of Pavia, Via Ferrata 1, 27100 Pavia, Italy); Faravelli, Lucia Source: Structural Control and Health Monitoring, v 16, n 7-8, p 751-765, November-December 2009, Takuji Kobori Memorial Issue
20. 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09
Source: 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09, 2009, 2009 IEEE Avionics, Fiber-Optics and Photonics Technology Conference, AVFOP'09
21. Advances in the development of pseudo-negative-stiffness dampers for seismic response control
Iemura, Hirokazu (Department of Urban Management, Kyoto University, Kyoto 615-8540, Japan); Pradono, Mulyo Harris Source: Structural Control and Health Monitoring, v 16, n 7-8, p 784-799, November-December 2009, Takuji Kobori Memorial Issue
22. Peak strain detection in EFPI sensors via direct phase difference synthesis
Mitchell, Kyle (Center for Sensors and Sensor Systems, Dept. Electrical and Computer Engineering, Saint Louis University, St. Louis, MO, United States); Ebel, William J. Source: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, p 370-374, 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems, ITSC '09
23. Adaptive passive, semiactive, smart tuned mass dampers: Identification and control using empirical mode decomposition, hilbert transform, and short-term fourier transform
Nagarajaiah, S. (Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, United States) Source: Structural Control and Health Monitoring, v 16, n 7-8, p 800-841, November-December 2009, Takuji Kobori Memorial Issue
24. A data-based procedure for analyzing the response of uncertain nonlinear systems
Masri, S.F. (Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089-2531, United States); Ghanem, R.; Arrate, F.; Caffrey, J.P. Source: Structural Control and Health Monitoring, v 16, n 7-8, p 724-750, November-December 2009, Takuji Kobori Memorial Issue
25. Multi-mode control performance of nonlinear dampers in stay cable vibrations
Hoang, Nam (Faculty of Civil Engineering, Ho Chi Minh City University of Technology, 268 Ly Thuong Kiet, Ho Chi Minh City, Viet Nam); Fujino, Yozo Source: Structural Control and Health Monitoring, v 16, n 7-8, p 860-868, November-December 2009, Takuji Kobori Memorial Issue

SHM ref

2010-01-03T04:05:00.001-08:00

1. Reliability assessment of long span bridges based on structural health monitoring: Application to Yonghe Bridge
Li, Shunlong (School of Transportation Science and Engineering, Harbin Institute of Technology, 150090, China); Li, Hui; Ou, Jinping; Li, Hongwei Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
2. Strain properties analysis and wireless collection system of PVDF for structural local health monitoring of civil engineering structures
Yu, Yan (Department of Electronic Engineering, DaLian University of Technology, Dalian, 116024, China); Wang, Yang; Dong, Weijie; Jin, Yajing; Ou, Jinping Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
3. Distributed fiber-optic sensing system with OFDR and its applications to structural health monitoring
Murayama, H. (University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan); Kageyama, K.; Uzawa, K.; Igawa, H.; Omichi, K.; MacHijima, Y. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
4. Health monitoring of reinforced concrete structures based on PZT admittance signal
Wang, Dansheng (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074, China); Zhu, Hongping; Shen, Danyan; Ge, Dongdong Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
5. A new type of intelligent wireless sensing network for health monitoring of large-size structures
Lei, Ying (Department of Civil Engineering, Xiamen University, Xiamen, 361005, China); Liu, Ch.; Wu, D.T.; Tang, Y.L.; Wang, J.X.; Wu, L.J.; Jiang, X.D. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
6. Health monitoring of a continuous rigid frame bridge based on PZT impedance and strain measurements
Zhang, Junbing (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, 430074, China); Zhu, Hongping; Wang, Dansheng; He, Bo; Zhou, Huaqiang Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
7. Corrosion monitoring of reinforcing steel in RC beam by an intelligent corrosion sensor
Qiao, Guofu (School of Civil Engineering, Harbin Institute of Technology, Harbin,150090, China); Hong, Yi; Ou, Jinping Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
8. Prewarning of the China National Aquatics Center using Johnson transformation based statistical process control
Zhang, Deyi (School of Civil Engineering, Harbin Institute of Technology, Harbin, 150090, China); Bao, Yuequan; Li, Hui; Ou, Jinping Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
9. A new diagnostic method of bolt loosening detection for thermal protection systems
Xie, Weihua (Center for Composite Materials and Structure, Harbin Institute of Technology, Harbin, TX 150080, China); Meng, Songhe; Han, Jiecai; Du, Shanyi; Zhang, Boming; Yu, Dong Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
10. Online debonding detection in honeycomb sandwich structures using multi-frequency guided waves
Song, F. (Dept. of Applied Science, Univ. of Arkansas at Little Rock, Little Rock, AR 72204, United States); Huang, G.L.; Hu, G.K. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
11. A comparison of interrogation schemes for impact event monitoring using fiber Bragg gratings
Shin, C.S. (Department of Mechanical Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan); Chen, B.L. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
12. Ultra thin fiber laser accelerometer for structure vibration detection
Zhang, Wentao (Optoelectronic System Laboratory, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China); Li, Xuecheng; Zhang, Faxiang; Li, Fang; Liu, Yuliang Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
13. Guided wave propagation based damage detection in welded rectangular tubular structures
Lu, Xi (Department of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong); Lu, M.Y.; Zhou, L.M.; Su, Z.Q.; Cheng, Li; Ye, Lin; Meng, Guang Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
14. Axisymmetrical analysis of functionally graded circular piezoelectric plate by graded element using MATLAB
Shao, B.L. (Department of Civil Engineering, Zhejiang University, Hangzhou 310027, China); Xu, R.Q. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
15. Guided-wave-based detections of weld and crack in steel plates
Lu, Mingyu (Department of Mechanical Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong); Lu, Xi; Zhou, Limin; Su, Zhongqing; Ye, Lin Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
16. Nonparametric dynamic modeling of a non-linear frame structure with MR dampers
Xu, B. (College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China); Huang, Z.G.; Masri, S.F. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
17. Wireless communication systems for continuous multiparameter health monitoring
Rousselot, Jérôme (Centre Suisse d'Electronique et Microtechnique SA, Jaquet-Droz 1, 2002 Neuchâtel, Switzerland); Decotignie, Jean-Dominique Source: Proceedings - 2009 IEEE International Conference on Ultra-Wideband, ICUWB 2009, p 480-484, 2009, Proceedings - 2009 IEEE International Conference on Ultra-Wideband, ICUWB 2009
18. Simulation of fiber Bragg grating sensor for rebar corrosion
Geng, Jiang (Department of Civil Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China); Wu, Jin; Zhao, Xinming Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
19. Application of cement-based piezoelectric composites in acoustic emission detection for concrete
Qin, Lei (School of Civil Engineering and Architecture, University of Jinan, Jinan 250022, China); Peng, Yaping; Ren, Hongwei; Li, Zongjin Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
20. Modal macro-strain vector based damage detection methodology with long-gauge FBG sensors
Xu, Bin (College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China); Liu, Chongwu W.; Masri, Sami F. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
21. Occupational health and safety management systems: Comparison between BS OHSAS 18001: 2007 and Italian Decree 81/2008
Degan, G.A. (Dipartimento di Ingegneria Meccanica e Industriale, Università degli Studi Roma Tre, Italy); Lippiello, D.; Pinzari, M. Source: WIT Transactions on Biomedicine and Health, v 14, p 401-409, 2009, Environmental Health Risk V
22. A novel fiber Bragg grating sensor interrogator based on time division multiplexing technique
Dai, Yongbo (Department of Automatic Test and Control, Harbin Institute of Technology (HIT), No. 92 West DaZhi Street, Harbin, 150001, China); Zhang, Zhichun; Leng, Jinsong; Asundi, Anand Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
23. Application of fiber Bragg grating sensor for rebar corrosion
Geng, Jiang (Department of Civil Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China); Wu, Jin; Zhao, Xinming Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
24. Investigation and detection on corrosion of concrete structure in marine environment
Zhang, Dandan (Department of Civil Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China); Wu, Jin; Gao, Junqi Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7493, 2009, 2nd International Conference on Smart Materials and Nanotechnology in Engineering
25. Modeling and simulation of Lamb wave generation with piezoelectric plates
Von Ende, Sven (Institute of Mechanics, Helmut-Schmidt-University, University of the Federal Armed Forces, D-22043 Hamburg, Germany); Lammering, Rolf Source: Mechanics of Advanced Materials and Structures, v 16, n 3, p 188-197, April 2009, III ECCOMAS Thematic Conference on Smart Structures and Materials

ref

2009-12-25T17:43:00.001-08:00

1. Simple fiber Bragg grating sensing systems for structural health monitoring
Tosi, D. (Dept. of Electron., Politec. di Torino, Torino, Italy); Olivero, M.; Perrone, G.; Vallan, A.; Arcudi, L. Source: 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009, p 80-6, 2009
2. Long term operational modal analysis of a stadium grandstand to structural health monitoring purposes
Vanali, M. (Dipt. di Meccanica, Politec. di Milano, Milan, Italy); Cigada, A. Source: 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009, p 103-9, 2009
3. High resolution, dynamic strain measurements with continuous fiber Bragg gratings for structural health monitoring
Sang, A. (Luna Innovations Inc., Roanoke, VA, USA); Marsden, K.; Wolfe, M.; Gifford, D.; Froggatt, M.; Soller, B. Source: 2009 IEEE Avionics, Fiber-Optics and Phototonics and Photonics Technology Conference. AVFOP 2009, p 29-31, 2009
4. Guided wave excitation by a CLoVER transducer for structural health monitoring: theory and experiments
Salas, K.I. (Dept. of Aerosp. Eng., Univ. of Michigan, Ann Arbor, MI, USA); Cesnik, C.E.S. Source: Smart Materials and Structures, v 18, n 7, p 075005 (27 pp.), July 2009
5. 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009
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6. Optical fiber sensors for long- and short-term crack monitoring
Casalicchio, M.L. (Dept. of Electron., Politec. di Torino, Turin, Italy); Penna, A.; Perrone, G.; Vallan, A. Source: 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009, p 87-92, 2009
7. Sensor data collecting system for railway facilities
Watanabe, Y. (Railway Technical Research Institute, Facilities Management Systems, Kokubunji-shi, Tokyo, Japan); Sato, N.; Tsuchiya, R. Source: WIT Transactions on the Built Environment, v 108, p 647-658, 2009, Safety and Security Engineering III Language: English
8. Research on signal processing for bridge health monitoring
Chen, Jing (School of Automation, Wuhan University of Technology, WuHan, HuBei, 430070, China); Wan, Chun; Xiao, Chun; Li, Liqiao Source: ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments, p 4338-4342, 2009, ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments Language: English
9. Investigation of wind turbine blade monitoring based on optical fiber Brillouin sensor
Furong Zhang (Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding, China); Yongqian Li; Zhi Yang; Liping Zhang Source: 2009 International Conference on Sustainable Power Generation and Supply. SUPERGEN 2009, p 4 pp., 2009
10. Research on monitoring technology of bolt tightness degree based on wavelet analysis
Bao, Peng-Yu (Department of Automation Science and Electrical Engineering, BeiHang University, 100191 Beijing, China); Yuan, Mei; Fu, Zhong Source: ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments, p 4329-4333, 2009, ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments Language: English
11. Real-Time monitoring of hurricane winds using wireless and sensor technology
Otero, Carlos E. (Florida Institute of Technology/Electrical and Computer Engineering, Melbourne, FL, United States); Velazquez, Antonio; Kostanic, Ivica; Subramanian, Chelakara; Pinelli, Jean-Paul; Buist, Larry Source: Journal of Computers, v 4, n 12, p 1275-1285, December 2009 Language: English
12. Air quality control for health care centres. The application of an intelligent distributed system
Di Lecce, V. (DIASS, Taranto, Italy); Amato, A.; Martines, C.; di Bari, P.; Dario, R. Source: 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009, p 27-30, 2009
13. Multi-channel data-acquisition and controller for mobile health monitoring system with HSDPA and GPS
Wu, Yu-Chi (Department of Electrical Engineering, National United University); Chen, Pei-Fen Source: 2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009, p 227-231, 2009, 2nd International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2009 Language: English
14. A method of high efficiency ultrasonic excitation
Zhixue, Shao (Nanjing Engineering Institute, No. 1 Haifuxiang, Nanjing 210007, China); Lihua, Shi; Haitao, Zhang; Qin, Yin Source: ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments, p 1479-1482, 2009, ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments Language: English
15. Study on genetic algorithm used in damage identification
Du, Chong (School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China); Yuan, Mei; You, Qiang Source: ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments, p 1404-1407, 2009, ICEMI 2009 - Proceedings of 9th International Conference on Electronic Measurement and Instruments Language: English
16. Smart high-performance materials for the multi-hazard protection of civil infrastructure
Messervey, T.B. (D'Appolonia S.p.A., Genova, Italy); Zangani, D.; Casciati, S. Source: WIT Transactions on the Built Environment, v 108, p 21-30, 2009, Safety and Security Engineering III Language: English
17. Studies on methodological developments in structural damage identification
Srinivas, V. (Structural Engineering Research Centre (Council of Scientific and Industrial Research), CSIR Campus, TTTI(Post), Taramani, Chennai-600 113, India); Sasmal, Saptarshi; Ramanjaneyulu, K. Source: SDHM Structural Durability and Health Monitoring, v 5, n 2, p 133-160, 2009 Language: English
18. A Lamb-wave-based technique for damage detection in composite laminates
Ng, C.T. (Sch. of Eng., Univ. of Queensland, Brisbane, QLD, Australia); Veidt, M. Source: Smart Materials and Structures, v 18, n 7, p 074006 (12 pp.), July 2009
19. Tensile fatigue properties of fibre Bragg grating optical fibre sensors
Ang, J. (School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, GPO Box 2476V, Melbourne, Vic. 3001, Australia); Li, H.C.H.; Herszberg, I.; Bannister, M.K.; Mouritz, A.P. Source: International Journal of Fatigue, v 32, n 4, p 762-768, April 2010 Language: English
20. Progressive collapse of a two-story reinforced concrete frame with embedded smart aggregates
Laskar, A. (Dept. of Civil & Environ. Eng., Univ. of Houston, Houston, TX, USA); Haichang Gu; Mo, Y.L.; Gangbing Song Source: Smart Materials and Structures, v 18, n 7, p 075001 (10 pp.), July 2009
21. Evaluation of long-term exposure to pollutants by means of a dispersion model
Latini, G. (Dipt. di Energetica, Univ. Politec. delle Marche, Ancona, Italy); Passerini, G.; Principi, F. Source: 2009 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems. EESMS 2009, p 31-8, 2009
22. Temperature sensitivity assessment of vibration-based damage identification techniques
Serker, N.H.M. Kamrujjaman (Department of Urban and Civil Engineering, Ibaraki University, 4-12-1 NakaNarusawa, Hitachi, Ibaraki 316-0036, Japan); Wu, Zhishen Source: SDHM Structural Durability and Health Monitoring, v 5, n 2, p 87-107, 2009 Language: English
23. Fatigue crack growth modelling of aluminium alloy under constant and variable amplitude loadings
Abdullah, S. (Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia); Beden, S.M.; Ariffin, A.K.; Al-Asady, N.A. Source: SDHM Structural Durability and Health Monitoring, v 5, n 2, p 109-131, 2009 Language: English
24. A simple model for the evaluation of constitutive laws for the computer simulation of fatigue-driven delamination in composite materials
Galvanetto, Ugo (Department of Aeronautics, Imperial College London, South Kensington Campus, South Kensington SW7 2AZ, United Kingdom); Robinson, Paul; Cerioni, Agostino; Armas, Carlos Lopez Source: SDHM Structural Durability and Health Monitoring, v 5, n 2, p 161-189, 2009 Language: English
25. Precision of syndesmophyte volume measurement for ankylosing spondylitis: a phantom study using high resolution CT
Tan, S. (Clinical Center, Nat. Institutes of Health, Bethesda, MD, USA); Jianhua Yao; Yao, L.; Ward, M.M. Source: 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2009, p 3577-80, 2009

ref

2009-12-25T17:42:00.001-08:00

1. Structural health monitoring of composites using integrated and flexible piezoelectric ultrasonic transducers
Kobayashi, M. (Ind. Mater. Inst., Nat. Res. Council of Canada, Boucherville, QC, Canada); Kuo-Tng Wu; Li Song; Cheng-Kuei Jen; Mrad, N. Source: Journal of Intelligent Material Systems and Structures, v 20, n 8, p 969-77, May 2009
2. Impedance-based structural health monitoring of a ceramic matrix composite
Gyekenyesi, A.L. (Ohio Aerosp. Inst., Cleveland, OH, USA); Martin, R.E.; Morscher, G.N.; Owen, R.B. Source: Journal of Intelligent Material Systems and Structures, v 20, n 7, p 875-82, May 2009
3. Ultra low-cost adaptations of electro-mechanical impedance technique for structural health monitoring
Bhalla, S. (Dept. of Civil Eng., Indian Inst. of Technol. Delhi, New Delhi, India); Gupta, A.; Bansal, S.; Garg, T. Source: Journal of Intelligent Material Systems and Structures, v 20, n 8, p 991-9, May 2009
4. Acoustic emission monitoring for detecting structural defects in vessels and offshore structures
Anastasopoulos, A. (Envirocoustics ABEE, Metamorphosis, Athens, Greece); Kourousis, D.; Botten, S.; Wang, G. Source: Ships and Offshore Structures, v 4, n 4, p 363-372, December 2009 Language:English
5. Initial performance of the lower deck of the Centre Street Bridge, Calgary
Shrive, P.L. (Dept. of Civil Eng., Univ. of Calgary, Calgary, AB, Canada); Brown, T.G.; Newhook, J.P.; Kroman, J.; Tadros, G.S.; Shrive, N.G. Source: Canadian Journal of Civil Engineering, v 36, n 1, p 50-62, Jan. 2009
6. Construction of a wireless sensor networking platform with vibration sensing and GPS positioning
Chau-Chung Song (Dept. of Aeronaut. Eng., Nat. Formosa Univ., Yunlin, Taiwan); Yin-Chieh Hsu; Chen-Fu Feng; Yu-Kai Chen Source: 2009 ICROS-SICE International Joint Conference. ICCAS-SICE 2009, p 5570-5, 2009
7. Development of guided wave simulator and application to sizing methodologies of pipe wall thinning
Kojima, F. (Grad. Sch. of Eng., Kobe Univ., Kobe, Japan); Tsubota, J.-i. Source: 2009 ICROS-SICE International Joint Conference. ICCAS-SICE 2009, p 2327-32, 2009
8. Condition monitoring and diagnosis for subsea control systems. A subsystem prototype
Altamiranda, E. (VetcoGray a GE Oil and Gas Business, Subsea Control Systems Engineering Department, Stavanger 4084, Norway); Kiaer, L.; Hu, X. Source: OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs, 2009, OCEANS '09 IEEE Bremen: Balancing Technology with Future Needs Language:English
9. Gaussian process time series model for life prognosis of metallic structures
Mohanty, S. (Mech. & Aerosp. Eng., Arizona State Univ., Tempe, AZ, USA); Das, S.; Chattopadhyay, A.; Peralta, P. Source: Journal of Intelligent Material Systems and Structures, v 20, n 8, p 887-96, May 2009
10. Cross-layer fault tolerant data aggregation for improved network delay in healthcare management applications
Xiaojing Yuan (Univ. of Houston, Houston, TX, USA); Haoying Liu Source: 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC 2009, p 1683-6, 2009
11. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors
Wahl, R.L. (Dept. of Radiol., Johns Hopkins Univ. Sch. of Med., Baltimore, MD, USA); Jacene, H.; Kasamon, Y.; Lodge, M.A. Source: Journal of Nuclear Medicine, v 50, Suppl.1, p 122-50S, May 2009

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2009-12-11T21:29:00.000-08:00

1. Structural health monitoring using an array of spatially distributed sensors
Vasilievici, A. (Sensors & Wireless Lab. Bucharest, ACS Sensors & Wireless Lab., Bucharest, Romania); Costea, S. Source: Proceedings of the 2009 32nd International Semiconductor Conference. CAS 2009, p 521-4, 2009
2. Analysis on non-linear characteristics of bridge health monitoring based on time-delayed transfer entropy and mutual information
Zhou Jianting (Inst. of Civil Eng., Chongqing Jiaotong Univ., Chongqing, China); Yang Jianxi Source: Proceedings of the 2009 Fifth International Joint Conference on INC, IMS and IDC, p 647-52, 2009
3. Capacitive MEMS energy harvesters for structural monitoring: design and fabrication
De Pasquale, G. (Mech. Dept., Politec. di Torino, Torino, Italy); Mian Wei; Soma, A.; Jing Wang Source: Proceedings of the 2009 32nd International Semiconductor Conference. CAS 2009, p 211-14, 2009
4. The combat-wireless health monitoring system
Burns, Maj Phillip G. (U.S. Army's School of Technology, Fort Gordon, GA, United States) Source: CrossTalk, v 22, n 11-12, p 4-9, November-December 2009 Language: English
5. Agent-based Web health care systems for real-time chronic disease
Yupeng Zhang (Res. Center of Ind. Technol., ChonBuk Nat. Univ., JeonJu, South Korea); Lee, M.; Gatton, T.M. Source: Proceedings of the 2009 IEEE Congress on Services. SERVICES 2009, p 14-21, 2009
6. Ubiquitous health monitoring system for multiple users using a ZigBee and wlan dual-network
Dae Cha, Yong (Broadcasting and Communications Fusion Program, Graduate School of Nano Technology Information Technology, and Design Technology Fusion Technology, Seoul National University of Technology, Seoul, Korea, Republic of); Yoon, Gilwon Source: Telemedicine and e-Health, v 15, n 9, p 891-897, November 1, 2009 Language: English
7. Exploring key factors in the choice of e-health using an asthma care mobile service model
Lin, Shu-Ping (Institute of Technology Management, Chung-Hua University, Hsin-Chu, Taiwan); Yang, Hung-Yu Source: Telemedicine and e-Health, v 15, n 9, p 884-890, November 1, 2009 Language: English
8. Validated caloric expenditure estimation using a single body-worn sensor
Lester, Jonathan (Computer Science and Engineering, University of Washington Seattle, Washington, DC, United States); Hartung, Carl; Pina, Laura; Libby, Ryan; Borriello, Gaetano; Duncan, Glen Source: ACM International Conference Proceeding Series, p 225-234, 2009, UbiComp'09 - Proceedings of the 11th ACM International Conference on Ubiquitous Computing Language: English
9. MagIC system. Advancements in the monitoring of cardiorespiratory function in active people by textile technology
Di Rienzo, M. (Biomed. Technol. Dept., Fondazione Don Carlo Gnocchi, Milan, Italy); Rizzo, F.; Meriggi, P.; Castiglioni, P.; Mazzoleni, P.; Parati, G.; Bordoni, B.; Brambilla, G.; Ferratini, M. Source: IEEE Engineering in Medicine and Biology Magazine, v 28, n 6, p 35-40, Nov.-Dec. 2009
10. Hot target inspection using a welded fibre acoustic wave piezoelectric sensor and a laser-ultrasonic mirror scanner
Chen Ciang Chia (Dept. of Aerosp. Eng., Chonbuk Nat. Univ., Jeonju, South Korea); Jung-Ryul Lee; He-Jin Shin Source: Measurement Science & Technology, v 20, n 12, p 127003 (8 pp.), Dec. 2009
11. Systematic review of telemedicine services for patients affected by chronic obstructive pulmonary disease (COPD)
Bartoli, Laura (Department of Management, Economics, and Industrial Engineering, Politecnico di Milano, 32 Piazz Leonardo da Vinci, Milan 20133, Italy); Zanaboni, Paolo; Masella, Cristina; Ursini, Niccoló Source: Telemedicine and e-Health, v 15, n 9, p 877-883, November 1, 2009 Language: English
12. IEEE Standard Criteria for Safety Systems for Nuclear Power Generating Stations
Source: , IEEE Std 603-2009, x+36 pp, 5 Nov. 2009
13. Using accelerometers for physical actions recognition by a neural fuzzy network
Liu, Shing-Hong (Department of Computer Science and Information Engineering, Chaoyang University of Technology, 168 Jifong E. Road, Wufong Township, Taichung, 43349, Taiwan); Chang, Yuan-Jen Source: Telemedicine and e-Health, v 15, n 9, p 867-876, November 1, 2009 Language: English
14. Performance impact analysis with KPP using application response measurement in E-government systems
Namho Yoo (DoD/HA, Falls Church, VA, USA) Source: Proceedings of the 2009 IEEE International Conference on Software Maintenance (ICSM 2009), p 503-6, 2009

Ref

2009-12-05T06:47:00.000-08:00

1. Investigation concerning structural health monitoring of an instrumented cable-stayed bridge

Ko, J.M. (Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong); Ni, Y.Q.; Zhou, H.F.; Wang, J.Y.; Zhou, X.T. Source: Structure and Infrastructure Engineering, v 5, n 6, p 497-513, December 2009

2. Recent enhancements to the SmartBrick structural health monitoring platform

Harms, T. (Dept. of Electr. & Comput. Eng., Missouri Univ. of Sci. & Technol., Rolla, MO, USA); Bastianini, F.; Sedigh, S. Source: Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC), p 6 pp., 2009

3. Optical fiber loop-sensors for structural health monitoring of composites

Nguyen, Nguyen Q. (Mechanical and Aerospace Engineering Department, Composite Materials and Mechanics Laboratory, Polytechnic Institute, Brooklyn, NY 11201, United States); Gupta, Nikhil Source: Materials Research Society Symposium Proceedings, v 1129, p 317-324, 2009, Materials Research Society Symposium Proceedings - Materials and Devices for Smart Systems III

4. Structural health monitoring and structural identification for long-span bridges

Grimmelsman, K.A. (Dept. of Civil Eng., Univ. of Arkansas, Fayetteville, AR, USA) Source: Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC), p 6 pp., 2009

5. Conformal passive sensors for wireless structural health monitoring

Balasubramaniam, Sharavanan (Department of Plastics Engineering, University of Massachusetts, Lowell, MA 01854, United States); Park, Jung-Rae; Mistry, Tarisha; Angkawisittpan, Niwat; Akyurtlu, Alkim; Rao, Tenneti; Nagarajan, Ramaswamy Source: Materials Research Society Symposium Proceedings, v 1129, p 341-348, 2009, Materials Research Society Symposium Proceedings - Materials and Devices for Smart Systems III

6. A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

Jahanshahi, Mohammad R. (Department of Civil and Environmental Engineering, University of Southern California Los Angeles, California, United States); Kelly, Jonathan S.; Masri, Sami F.; Sukhatme, Gaurav S. Source: Structure and Infrastructure Engineering, v 5, n 6, p 455-486, December 2009

7. Continuous monitoring of the Øresund Bridge: System and data analysis

Peeters, Bart (LMS International, Interleuvenlaan 68, B-3001 Leuven, Belgium); Couvreur, G.; Razinkov, O.; Kündig, C.; van der Auweraer, H.; de Roeck, G. Source: Structure and Infrastructure Engineering, v 5, n 5, p 395-405, October 2009

8. Portable embedded medical box implementation for health monitoring applications in avionic environments

Anastasopoulos, Konstantinos (Electronic Computing System department, Technological Educational Institute of Piraeus, Athens, Greece); Kouros, Pavlos; Marinos, Dimitrios; Aidinis, Konstantinos; Schmitt, Nikolaus; Klaue, Jirka; Pistner, Thomas Source: 2009 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2009, p 17-21, 2009, 2009 IEEE International Workshop on Medical Measurements and Applications, MeMeA 2009

9. Necessity of the bridge health monitoring system to mitigate natural and man-made disasters

Chang, Sung-Pil (Seoul National University, Seoul, Korea, Republic of); Yee, Jaeyeol; Lee, Jungwhee Source: Structure and Infrastructure Engineering, v 5, n 3, p 173-197, June 2009

10. Research on monitoring technology of bolt tightness degree based on wavelet analysis

Peng-yu Bao (Dept. of Autom. Sci. & Electr. Eng., BeiHang Univ., Beijing, China); Mei Yuan; Zhong Fu Source: Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009), p 4/329-33, 2009

11. Sensor system selection for prognostics and health monitoring

Cheng, Shunfeng (Proanostics and Health Manaqement Lab); Azarian, Michael; Pechf, Michael Source: 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008, v 3, n PART B, p 1383-1389, 2009, 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008

12. Hybrid health monitoring of prestressed concrete girder bridges by sequential vibration-impedance approaches

Kim, Jeong-Tae (Department of Ocean Engineering, Pukyong National University, Busan, Korea, Republic of); Park, Jae-Hyung; Hong, Dong-Soo; Park, Woo-Sun Source: Engineering Structures, v 32, n 1, p 115-128, January 2010

13. Return on experience on tucson pipe risk management system

Paul, Acosta (Tucson Water Authority, Tucson, AZ, United States); Britt, Klein; Vincent, Morisseau; Benoit, Kroely Source: Pipelines 2009: Infrastructure's Hidden Assets - Proceedings of the Pipelines 2009 Conference, v 360, p 1253-1262, 2009, Pipelines 2009: Infrastructure's Hidden Assets - Proceedings of the Pipelines 2009 Conference

14. Study on system of fiber optic strain signal sampling and processing based on LabVIEW

Lang Li-ying (Hebei Univ. of Eng., Handan, China); Shen Lan Source: Proceedings of the 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), p 4 pp., 2009

15. An event-driven context model in elderly health monitoring

Yuanyuan Cao (Tsinghua Nat. Lab. for Inf. Sci. & Technol. (TNList), Tsinghua Univ., Beijing, China); Linmi Tao; Guangyou Xu Source: 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing in Conjunction with The UIC 2009 and ATC 2009 Conferences, p 120-4, 2009

16. Acoustic-wave-mode separation using a distributed Bragg grating sensor

Rajic, N. (Defence Science and Technology Organisation, 506 Lorimer Street, Fishermans Bend, VIC 3207, Australia); Davis, C.; Thomson, A. Source: Smart Materials and Structures, v 18, n 12, 2009

17. Research on signal processing for bridge health monitoring

Jing Chen (Sch. of Autom., Wuhan Univ. of Technol. Univ., Wuhan, China); Chun Wan; Chun Xiao; Liqiao Li Source: Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009), p 4-338-42, 2009

18. Mobile health monitoring and smart sensors: A product line approach

Laguna, Miguel A. (GIRO, University of Valladolid, 47011 Valladolid, Spain); Finat, Javier; González, José A. Source: Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to Increase Digital Citizenship, EATIS '09, 2009, Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to Increase Digital Citizenship, EATIS '09

19. Two-dimensional periodic actuators for frequency-based beam steering

Romanoni, M. (Dipartimento di Meccanica, Politecnico di Torino, Torino, Italy); Gonella, S.; Apetre, N.; Ruzzene, M. Source: Smart Materials and Structures, v 18, n 12, 2009

20. Study on genetic algorithm used in damage identification

Chong Du (Sch. of Autom. Sci. & Electr. Eng., Beijing Univ. of Aeronaut.&Astronaut., Beijing, China); Mei Yuan; Qiang You Source: Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009), p 1/404-7, 2009

21. Statistical novelty detection within the Yeongjong suspension bridge under environmental and operational variations

Oh, Chang Kook (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of); Sohn, Hoon; Bae, In-Hwan Source: Smart Materials and Structures, v 18, n 12, 2009

22. 3D integration technologies for ceramic substrates in a SHM application

Hildebrandt, Samuel (Dresden University of Technology, Electronics Packaging Laboratory, 01062 Dresden, Germany); Wolter, Klaus-Jürgen Source: 2009 European Microelectronics and Packaging Conference, EMPC 2009, 2009, 2009 European Microelectronics and Packaging Conference, EMPC 2009

23. Traffic-aware relay node deployment for data collection in wireless sensor networks

Wang, Feng (School of Computing Science, Simon Fraser University, BC, Canada); Wang, Dan; Liu, Jiangchuan Source: 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2009, 2009, 2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, SECON 2009

24. Structural identification using a low-cost search method

Fonda, J.W. (Electr. Eng., Univ. of Missouri-Rolla, Rolla, MO, USA); Watkins, S.E. Source: Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems (ITSC), p 5 pp., 2009

25. Carbon nanofiber-network sensor films for strain measurement in composites

Nguyen, Nguyen Quang (Composite Materials and Mechanics Laboratory, Mechanical and Aerospace Engineering Department, Polytechnic Institute of New York University, Brooklyn, NY 11201, United States); Lee, Sangyoon; Gupta, Nikhil Source: Materials Research Society Symposium Proceedings, v 1129, p 53-59, 2009, Materials Research Society Symposium Proceedings - Materials and Devices for Smart Systems III

Mobile Bridge Health Monitoring Lab

2009-12-03T22:14:00.000-08:00

"Among the equipment in the lab is a laser Doppler vibrometer, which measures the speed and displacement of a vibrating structure; various strain gauges that assess the stress, strain and loading on a structure; and an impulse hammer that measures how much force is applied in tests under controlled conditions. Also available are accelerometers, linear variable differential transducers, and a portable weigh-in-motion system. Some of the equipment is wireless, so students can be sitting here in a computer lab on campus and continually monitor the stress levels on a bridge almost anywhere,”

http://pda.physorg.com/_news178991175.html

SHM ref

2009-11-26T17:54:00.000-08:00

1. Development of a baseline for structural health monitoring for a curved post-tensioned concrete box-girder bridge

Liu, Chengyin (Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, United States); DeWolf, John T.; Kim, Jeong-Ho Source: Engineering Structures, v 31, n 12, p 3107-3115, December 2009

2. Wireless sensor networks in structural health monitoring based on ZigBee technology

Xiang-dong Jiang (Dept. of Commun. Eng., Xiamen Univ., Xiamen, China); Yu-liang Tang; Ying Lei Source: 2009 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication (2009 ASID), p 4 pp., 2009

3. A study and application of HHT in vibration signal analysis of bridge structural health monitoring system

Ding Pei (Comput. & Inf. Eng. Coll., Beijing Technol. & Bus. Univ., Beijing, China); Yu Chongchong; He Xiaoyu Source: Proceedings of the 2009 2nd International Congress on Image and Signal Processing (CISP), p 5 pp., 2009

4. A structural health monitoring system using wireless sensor network

Jianjun Niu (Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China); Zhidong Deng; Fengguang Zhou; Zongsheng Cao; Zhanqing Liu; Fangzhou Zhu Source: Proceedings of the 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), p 4 pp., 2009

5. Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring

Wu, Jian (The Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29# Yu Dao Street, Nanjing, 210016, China); Yuan, Shenfang; Ji, Sai; Zhou, Genyuan; Wang, Yang; Wang, Zilong Source: Expert Systems with Applications, v 37, n 3, p 2028-2036, March 15, 2010

6. Evaluation and calibration of FBG-based relative humidity sensor designed for structural health monitoring

Venugopalan, T. (School of Engineering and Mathematical Sciences, City University London, Northampton Square, London EC1V 0HB, United Kingdom); Yeo, T.L.; Basedau, F.; Henke, A.S.; Sun, T.; Grattan, K.T.V.; Habel, W. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

7. A multi-agent architecture of health monitoring system for long-span bridge

He, X.B. (Sch. of Civil Eng. & Archit., Chongqing Jiaotong Univ., Chongqing, China); Ning, J.C. Source: Proceedings of the 2009 International Conference on Management and Service Science (MASS), p 4 pp., 2009

8. Utilization of wireless sensor network for health monitoring in home environment

Zakrzewski, Mari (Department of Electronics, Tampere University of Technology (TUT), Finland); Junnila, Sakari; Vehkaoja, Antti; Kailanto, Harri; Vainio, Antti-Matti; Defee, Irek; Lekkala, Jukka; Vanhala, Jukka; Hyttinen, Jari Source: Proceedings - 2009 IEEE International Symposium on Industrial Embedded Systems, SIES 2009, p 132-135, 2009, Proceedings - 2009 IEEE International Symposium on Industrial Embedded Systems, SIES 2009

9. Research on prognostic and health monitoring system for large complex equipment

Zhang, Jinyu (Xi'an Research Institute of High-tech, Xi'an, China); Huang, Xianxiang; Cai, Wei Source: Proceedings - 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009, p 3-8, 2009, Proceedings - 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009

10. Remote wireless health monitoring systems

Priya, B. (Faculty of Engineering, University Industry Selangor, Malaysia); Rajendran, S.; Bala, R.; Gobbi, R. Source: 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, p 383-388, 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009

11. FBG application in bridge health monitoring system of Wuhan Yangtze River 2nd Bridge

Liu, Jun (Fiber Optic Sensing Technology Research Center, WuHan University of Technology, 430070 Wuhan, China) Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

12. Data disposal and analysis with least squares collocation in tall building health monitoring

Xing-hai Dang (Civil Eng. Sch., Lanzhou Univ. of Technol., Lanzhou, China); Yu-ming Wei; Jian-yun Zhao; Yu-li Yang Source: Proceedings of the 2009 International Conference on Management and Service Science (MASS), p 4 pp., 2009

13. Enhanced SEA-MAC: An efficient MAC protocol for wireless sensor networks for environmental monitoring applications

Shukur, Marwan Ihsan (Universiti Teknologi PETRONAS, Department of Electrical and Electronic Engineering, Tronoh, 31750, Perak, Malaysia); Yap, Vooi Voon Source: 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009, p 182-185, 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2009

14. Parametric identification of structures with nonlinearities using global and substructure approaches in the time domain

Kumar, R. Kishore (Machine Design Section, Department of Mechanical Engineering, IIT Madras, India); Shankar, K. Source: Advances in Structural Engineering, v 12, n 2, p 195-210, April 2009

15. Design and validation of wireless dynamic testing system for bridge based on the 941B type vibration sensor

Zhonglong, Li (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China); Lianzhen, Zhang; Xiaodi, Zhu; Hongjiang, Gu; Zitian, Jiang Source: Proceedings of the 9th International Conference of Chinese Transportation Professionals, ICCTP 2009: Critical Issues in Transportation System Planning, Development, and Management, v 358, p 211-218, 2009, Proceedings of the 9th International Conference of Chinese Transportation Professionals, ICCTP 2009: Critical Issues in Transportation System Planning, Development, and Management

16. Health monitoring of engine blades by using an in-line fiber-optic F-P strain sensor based on hollow-core photonic crystal fiber

Rao, Yunjiang (Key Laboratory of Optoelectronic Technology and Systems (Ministry of Education), Chongqing University, Chongqing 400044, China); Fan, Yanen; Li, Hong; Zhu, Tao Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

17. Monitoring the effects of impact damages on modal parameters in carbon fiber entangled sandwich beams

Shahdin, Amir (Université de Toulouse, ISAE, DMSM, Campus Supaero, 10 av. Edouard Belin BP54032, 31055 Toulouse, France); Mezeix, Laurent; Bouvet, Christophe; Morlier, Joseph; Gourinat, Yves Source: Engineering Structures, v 31, n 12, p 2833-2841, December 2009

18. Damage diagnosis for wind turbine blades based on the shifting distance of characteristic frequency

Zhiqiang Wang (Sch. of Mech. & Electron. Eng., Tianjin Polytech. Univ., Tianjin, China); Yuxiu Xu; Yuanying Mei Source: Proceedings of the 2009 2nd International Congress on Image and Signal Processing (CISP), p 3 pp., 2009

19. Design and study of fiber optic strain demodulation system based on LabVIEW

Lang, Li-Ying (Hebei University of Engineering, Handan, Hebei province, 056038, China); Lan, Shen; Zhao, Jian-Jiao Source: 2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009, v 1, p 288-291, 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2009

20. Measurement of distributed strain and load identification using 1500 mm gauge length FBG and optical frequency domain reflectometry

Igawa, Hirotaka (Japan Aerospace Exploration Agency, 6-13-1 Osawa, Mitaka, Tokyo 181-0015, Japan); Murayama, Hideaki; Nakamura, Toshiya; Yamaguchi, Isao; Kageyama, Kazuro; Uzawa, Kiyoshi; Wada, Daichi; Ohsawa, Isamu; Kanai, Makoto; Omichi, Koji Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

21. Application of FBG sensors in strengthening and maintenance monitoring of old bridges

Yue, Li-Na (Key Laboratory of Fiber, Wuhan University of Technology, Ministry of Education, Wuhan, 430070, China); Huang, Jun; Yang, Yan Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

22. A decentralized Gauss-Seidel approach for in-network sparse signal recovery

Ling, Qing (Department of ECE, Michigan Technological University, Houghton, MI, United States); Tian, Zhi Source: 2009 12th International Conference on Information Fusion, FUSION 2009, p 380-387, 2009, 2009 12th International Conference on Information Fusion, FUSION 2009

23. Photonic skins for optical sensing: Highlights of the PHOSFOS Project

Berghmans, F. (Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium); Thienpont, H.; Van Daele, P.; Dubruel, P.; Urbanczyk, W.; Rayss, J.; Webb, D.J.; Vlekken, J.; Kalli, K.; Sugden, K. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

24. Benchmarking the response of Bragg gratings written in micro-structured and bow tie fiber embedded in composites

Luyckx, G. (Dept of Materials Science and Eng., Ghent University, 41 St-Pietersnieuwstr, Gent 9000, Belgium); Voet, E.; Geernaert, T.; Chah, K.; Nasilowski, T.; De Waele, W.; Becker, M.; Bartelt, H.; Urbanczyk, W.; Wojcik, J.; Degrieck, J.; Berghmans, F.; Thienpont, H. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7503, 2009, 20th International Conference on Optical Fibre Sensors

25. Comprehensive evaluation on the durability of concrete structure base on uncertain measurements

Chang-long, Wang (School of Civil Engineering, Hebei University of Engineering, Handan, China) Source: Proceedings - 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009, p 517-520, 2009, Proceedings - 2009 IITA International Conference on Control, Automation and Systems Engineering, CASE 2009

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1. A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks

Mascarenas, David (Sullivan International Group, SAIC, San Diego, CA 92108, United States); Flynn, Eric; Farrar, Charles; Park, Gyuhae; Todd, Michael Source: IEEE Sensors Journal, v 9, n 12, p 1719-1726, December 2009

2. Polymer optical fiber sensors for distributed strain measurement and application in structural health monitoring

Liehr, Sascha (BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, D-12205 Berlin, Germany); Lenke, Philipp; Wendt, Mario; Krebber, Katerina; Seeger, Monika; Thiele, Elke; Metschies, Heike; Gebreselassie, Berhane; Munich, Johannes Christian Source: IEEE Sensors Journal, v 9, n 11, p 1330-1338, November 2009

3. Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications

Gul, M. (Dept. of Civil, Environ. & Constr. Eng., Univ. of Central Florida, Orlando, FL, USA); Catbas, F.N. Source: Mechanical Systems and Signal Processing, v 23, n 7, p 2192-204, Oct. 2009

4. Energy harvesting wireless sensors and networked timing synchronization for aircraft structural health monitoring

Arms, S.W. (MicroStrain Inc., Williston, VT, United States); Galbreath, J.H.; Townsend, C.P.; Churchill, D.L.; Corneau, B.; Ketcham, R.P.; Phan, Nam Source: Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009, p 16-20, 2009, Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009

5. Bio-inspired sensor skins for structural health monitoring

Tata, Uday (Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, 500 West First Street, Arlington, TX 76019, United States); Deshmukh, S.; Chiao, J.C.; Carter, Ronald; Huang, H. Source: Smart Materials and Structures, v 18, n 10, 2009

6. Fibre-optic structural health monitoring in the energy industry

Ecke, Wolfgang (IPHT - Institute of Photonic Technology, Albert-Einstein-Str. 9, 07745 Jena, Germany); Willsch, Reinhardt; Bartelt, Hartmut Source: 2009 14th OptoElectronics and Communications Conference, OECC 2009, 2009, 2009 14th OptoElectronics and Communications Conference, OECC 2009

7. Design and evaluation of optical fibre sensors in civil engineering applications for structural health monitoring

Grattan, Kenneth T. V. (School of Engineering and Mathematical Sciences, City University London, Northampton Square, London, EC1V 0HB, United Kingdom); Kerrouche, Abdelfateh; Sun, Tong; Grattan, S.K.T.; Taylor, Susan E.; Basheer, P.A. Mohammed Source: 2009 14th OptoElectronics and Communications Conference, OECC 2009, 2009, 2009 14th OptoElectronics and Communications Conference, OECC 2009

8. Feasibility Study on a new energy harvesting electromagnetic device using aerodynamic instability

Jung, Hyung-Jo (Civil and Environmental Engineering Department, KAIST, Daejeon 305-701, Korea, Republic of); Lee, Seung-Woo; Jang, Dong-Doo Source: IEEE Transactions on Magnetics, v 45, n 10, p 4376-4379, October 2009

9. The application of 1-3 cement-based piezoelectric transducers in active and passive health monitoring for concrete structures

Qin, Lei (School of Civil and Architectural Engineering, University of Jinan, Jinan 250022, China); Huang, Shifeng; Cheng, Xin; Lu, Youyuan; Li, Zongjin Source: Smart Materials and Structures, v 18, n 9, 2009

10. Multimodal platform for communication, training and health monitoring at home

Ruminski, J. (Department of Biomedical Engineering, Gdansk University of Technology, Gdansk, Poland); Kocejko, T.; Bujnowski, A.; Wtorek, J. Source: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009, 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009

11. An experimental low-cost, low-data-rate rapid structural assessment network

Chin, Jren-Chit (Purdue University, West Lafayette, IN 47907, United States); Rautenberg, Jeffrey M.; Ma, Chris Y. T.; Pujol, Santiago; Yau, David K. Y. Source: IEEE Sensors Journal, v 9, n 11, p 1361-1369, November 2009

12. Realising management and composition of self-managed cells in pervasive healthcare

Schaeffer-Filho, Alberto (Department of Computing, Imperial College London, 180 Queen's Gate, SW7 2AZ, London, United Kingdom); Lupu, Emil; Sloman, Morris Source: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009, 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009

13. Designing for complex innovations in health care: Design theory and realist evaluation combined

Keller, Christina (Uppsala University, Jönköping International Business School, P.O. Box 513, Uppsala, SE-751 20, Sweden); Lindblad, Staffan; Gäre, Klas; Edenius, Mats Source: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, DESRIST '09, 2009, Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology, DESRIST '09

14. A novel time-domain structural parametric identification methodology based on the equivalency of neural networks and ARMA model

Xu, Bin (College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China); Gong, Ansu; He, Jia; Masri, Sami F. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 5754 LNCS, p 888-897, 2009, Emerging Intelligent Computing Technology and Applications - 5th International Conference on Intelligent Computing, ICIC 2009, Proceedings

15. Effects of noise on transfer entropy estimation for damage detection

Overbey, L.A. (Dept. of Struct. Eng., Univ. of California, La Jolla, CA, USA); Todd, M.D. Source: Mechanical Systems and Signal Processing, v 23, n 7, p 2178-91, Oct. 2009

16. A novel routing metric for environmentally-powered sensors with hybrid energy storage systems

Kailas, Aravind (School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0250, United States); Ingram, Mary Ann; Ying, Zhang Source: Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009, p 42-46, 2009, Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009

17. The Berkeley tricorder: Ambulatory health monitoring

Naima, Reza (University of California, Berkeley, Berkeley Institute of Design, 281 Hearst Memorial Mining Building #1764, Berkeley, CA 94720, United States); Canny, John Source: Proceedings - 2009 6th International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009, p 53-58, 2009, Proceedings - 2009 6th International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009

18. Distance similarity matrix using ensemble of dimensional data reduction techniques: vibration and aerocoustic case studies

Lopez, I. (Dept. of Mech. & Aeronaut. Eng., Univ. of California, Davis, CA, USA); Sarigul-Klijn, N. Source: Mechanical Systems and Signal Processing, v 23, n 7, p 2287-300, Oct. 2009

19. Piezoelectric active-sensor diagnostics and validation using instantaneous baseline data

Overly, Timothy G. (Los Alamos National Laboratory, Los Alamos, NM 87545, United States); Park, Gyuhae; Farinholt, Kevin M.; Farrar, Charles R. Source: IEEE Sensors Journal, v 9, n 11, p 1414-1421, November 2009

20. Wireless Sensor Networks Powered by Ambient Energy Harvesting (WSN-HEAP) - Survey and challenges

Seah, Winston K. G. (Networking Protocols Department, Institute for Infocomm Research, Singapore, Singapore); Zhi, Ang Eu; Tan, Hwee-Pink Source: Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009, p 1-5, 2009, Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009

21. Structural finite element model updating using transfer function data

Esfandiari, A. (Amirkabir University of Technology, 424, Hafez Ave, Tehran, Iran); Bakhtiari-Nejad, F.; Sanayei, M.; Rahai, A. Source: Computers and Structures, v 88, n 1-2, p 54-64, January 2010

22. Prediction and management in energy harvested wireless sensor nodes

Piorno, Joaquín Recas (DACYA, Complutense University of Madrid, Spain); Bergonzini, Carlo; Atienza, David; Rosing, Tajana Simunic Source: Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009, p 6-10, 2009, Proceedings of the 2009 1st International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace and Electronic Systems Technology, Wireless VITAE 2009

23. Unsupervised structure damage classification based on the data clustering and artificial immune pattern recognition

Chen, Bo (Department of Mechanical Engineering - Engineering Mechanics, Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI, United States); Zang, Chuanzhi Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 5666 LNCS, p 206-219, 2009, Artificial Immune Systems - 8th International Conference, ICARIS 2009, Proceedings

24. Flexible home care automation: Adapting to the personal and evolving needs and situations of the patient

Eslami, Mohammad Zarifi (Information Systems Group, University of Twente, Enschede, Netherlands) Source: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009, 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare - Pervasive Health 2009, PCTHealth 2009

25. Corrosion-technical properties of high-strength stainless steels for the application in prestressed concrete structures

Wu, Y. (VSL Schweiz AG, Industriestr. 14, 4553 Subingen, Switzerland); Nürnberger, U. Source: Materials and Corrosion, v 60, n 10, p 771-780, October 2009

SHM wins in science and technology competition.

2009-11-19T02:11:00.000-08:00

http://www.macaudailytimes.com.mo/macau/6013-wins-big-science-and-technology-competition.html

One entry “Ambient influences on structural health monitoring using Bayesian probabilistic approach”, from the Department of Civil and Environmental Engineering.

12 min webcast on NI SHM

2009-11-18T21:19:00.000-08:00

https://admin.na3.acrobat.com/_a56821929/p22158130/

The Future of SHM In China and India

2009-11-18T21:07:00.000-08:00

According to The World Bank, by 2015, half of the world’s new building construction will take place in China and India. Because most large structures located in countries such as the United States were built before the development of advanced monitoring systems, the opportunity is now available to monitor these buildings and perform research that will ultimately help improve the safety of future buildings and reduce the number of lives lost from catastrophic events.

Test bed for SHM technology in China

2009-11-18T20:52:00.001-08:00

In 2004, the China Earthquake Administration (CEA), the governmental body managing the country’s earthquake preparedness and disaster mitigation, selected seven newly constructed megastructures as the test bed for structural health monitoring (SHM) technology. These landmark structures include the 2008 Summer Olympic venues in Beijing (including both the Beijing National Stadium and the Beijing National Aquatics Center), the 104-story World Trade Center in Shanghai, the 66-story Park Hyatt Hotel complex in Beijing, the 240 m concrete arch dam in Ertan, the 8266 m cable-stayed bridge in Shantou, and the base-isolated CEA data center in Beijing.

Performing Structural Health Monitoring of the 2008 Olympic Venues Using NI LabVIEW and CompactRIO

2009-11-18T20:49:00.000-08:00

http://sine.ni.com/cs/app/doc/p/id/cs-11279

http://sine.ni.com/nips/cds/view/p/lang/en/nid/206817

http://zone.ni.com/wv/app/doc/p/id/wv-1325

1.5 min NI video

2009-11-18T20:36:00.000-08:00

http://www.ni.com/structural_health_monitoring/

Bridge Health Monitoring: Reality

2009-11-17T21:34:00.000-08:00

"When heavy trucks rumble across the Burlington-Bristol Bridge, new sensors detect the stresses they put on the 2,300-foot span and instantly transmit the information to computers in Philadelphia.
Drexel and its partner, the Burlington County Bridge Commission, plan to use the system continuously to help the agency better allocate its resources for bridge maintenance.
In Minnesota, for example, engineers are analyzing data from hundreds of sensors on the new Interstate 35 bridge erected last year in Minneapolis to replace the span that collapsed in August 2007, killing 13 people. Yet one challenge in monitoring long-standing bridges is understanding stresses that are inherent in the structure, according to French. She pointed out that one cause of the I-35 collapse was that its gusset plates were too thin - a design error committed a half-century ago and not necessarily detectable if sensors had been attached.
Others are exploring wireless sensors, which were used in a pilot project on the Golden Gate Bridge."

http://www.philly.com/inquirer/local/20091116_Bridge_sensors_send_stress_data_to_Drexel_researchers.html

Review

2009-11-13T22:48:00.000-08:00

1. Fundamental modeling issues on benchmark structure for structural health monitoring
Li HuaJun (Dept. of Ocean Eng., Ocean Univ. of China, Qingdao, China); Zhang Min; Wang JunRong; James, H.S.-L. Source: Science in China Series E: Technological Sciences, v 52, n 7, p 1999-2008, July 2009
2. A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks
Mascarenas, D. (Sullivan Int. Group, SAIC, San Diego, CA, USA); Flynn, E.; Farrar, C.; Park, G.; Todd, M. Source: IEEE Sensors Journal, v 9, n 12, p 1719-26, Dec. 2009
3. Localized structural health monitoring using energy-efficient wireless sensor networks
Qing Ling (Dept. of Electr. & Comput. Eng., Michigan Technol. Univ., Houghton, MI, USA); Zhi Tian; Yuejun Yin; Yue Li Source: IEEE Sensors Journal, v 9, n 11, p 1596-604, Nov. 2009
4. Autonomous sensor nodes for aircraft structural health monitoring
Becker, T. (EADS Innovation Works, Munich, Germany); Kluge, M.; Schalk, J.; Tiplady, K.; Paget, C.; Hilleringmann, U.; Otterpohl, T. Source: IEEE Sensors Journal, v 9, n 11, p 1589-95, Nov. 2009
5. Statistical classifiers for structural health monitoring
Tschope, C. (Fraunhofer-Inst. fur Zerstorungsfreie Prufverfahren, Dresden, Germany); Wolff, M. Source: IEEE Sensors Journal, v 9, n 11, p 1567-76, Nov. 2009
6. Design and Experiment of PZT Network-based Structural Health Monitoring Scanning System
Lei, Qiu (The Aeronautic Key Lab for Smart Material and Structure, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China); Shenfang, Yuan; Qiang, Wang; Yajie, Sun; Weiwei, Yang Source: Chinese Journal of Aeronautics, v 22, n 5, p 505-512, October 2009
7. Structural health monitoring for bell 407 tail boom
Purekar, Ashish (Techno-Sciences, Inc., Beltsville, MD, United States); Kothari, Kunal; Choi, Young-Tai; Wereley, Norman M.; Wilson, Henry; Bordick, Nathaniel Source: Annual Forum Proceedings - AHS International, v 2, p 946-954, 2009, 65th Annual Forum Proceedings - AHS International
8. Time reversal technique for health monitoring of metallic structure using Lamb waves
Gangadharan, R. (Department of Aerospace Engineering, Indian Institute of Science, Bangalore, 560 012, India); Murthy, C.R.L.; Gopalakrishnan, S.; Bhat, M.R. Source: Ultrasonics, v 49, n 8, p 696-705, December 2009
9. Detection of delamination damage in a composite laminate beam utilising the principle of strain compatibility
Wildy, Ben (School of Mechanical Engineering, University of Adelaide, SA 5005, Australia); Cazzolato, Andrei; Kotousov, Stuart Source: Key Engineering Materials, v 417-418, p 269-272, 2010
10. Health Monitoring System (HMS) for structural assessment
Campos e Matos, J. (Civil Eng. Dept., Univ. of Porto, Porto, Portugal); Garcia, O.; Henriques, A.A.; Casas, J.R.; Vehi, J. Source: Smart Structures and Systems, v 5, n 3, p 223-40, May 2009
11. Strain distribution monitoring wireless sensor network design and its evaluation research on aircraft wingbox
Wu, Jian (Aeronautic Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29# Yu Dao Street, Nanjing, 210016, China); Yuan, Shenfang; Shang, Yin; Wang, Zilong Source: International Journal of Applied Electromagnetics and Mechanics, v 31, n 1, p 17-28, 2009
12. Virtual sensors for diagnosis and prognosis purposes in the context of elastic mechanical structures
Heidtmann, F. (Dynamics & Control, Eng. Fac., Univ. of Duisburg-Essen, Duisburg, Germany); Soffker, D. Source: IEEE Sensors Journal, v 9, n 11, p 1577-88, Nov. 2009
13. Experimental study on strain and deformation monitoring of reinforced concrete structures using PPP-BOTDA
Guo Tong (Key Lab. of R.C. & P.C. Struct. of Minist. of Educ., Southeast Univ., Nanjing, China); Li Ai-Qun; Song Yong-Sheng; Zhang Biao; Liu Yang; Yu NingSheng Source: Science in China Series E: Technological Sciences, v 52, n 10, p 2859-68, Oct. 2009
14. A novel configuration-driven data mining framework for health and usage monitoring systems
Al-Kateb, Mohammed (Department of Computer Science, University of Vermont, Burlington VT 05405, United States); Joshi, Pradnya; Imadabathuni, Mahindra; Bechhoefer, Eric; He, David Source: Annual Forum Proceedings - AHS International, v 2, p 1680-1692, 2009, 65th Annual Forum Proceedings - AHS International
15. WIVA: WSN monitoring framework based on 3D visualization and augmented reality in mobile devices
Koo, Bonhyun (Convergence Lab, Digital MediaandCommunications RandD Center, Samsung Electronics, 416, Maetan-3dong, Yeongtong-gu, Suswon-si, Gyeonggi-do, Dong Suwon 442-600, Korea, Republic of); Choi, Hyohyun; Shon, Taeshik Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 5597 LNCS, p 158-165, 2009, Ambient Assistive Health and Wellness Management in the Heart of the City - 7th International Conference on Smart Homes and Health Telematics, ICOST 2009, Proceedings
16. Open systems capabilities and experience with the integrated vehicle health management system
Tayloe, Shawn (Goodrich Corporation, Vergennes, VT, United States); Flynn, Tim; Isom, Joshua Source: Annual Forum Proceedings - AHS International, v 2, p 1701-1708, 2009, 65th Annual Forum Proceedings - AHS International
17. Simultaneous edge debonding monitoring and repair of dynamically loaded concrete structures strengthened with smart composite materials
Providakis, C.P. (Department of Applied Science, Technical University of Crete, GR-73100 Chania, Greece) Source: Key Engineering Materials, v 417-418, p 33-36, 2010
18. Editorial special issue on sensors systems for structural health monitoring
Mukhopadhyay, Subhas Chandra (Massey University, School of Engineering and Advanced Technology, Palmerston North 5301, New Zealand); Chase, J. Geoffrey; Meyendorf, Norbert Source: IEEE Sensors Journal, v 9, n 11, p 1319-1321, November 2009
19. Design of an optical fibre sensor patch for longitudinal strain measurement in structures
Ramos, C.A. (Inst. Super. de Eng. do Porto (ISEP), Porto, Portugal); de Oliveira, R.; Marques, A.T. Source: Materials & Design, v 30, n 7, p 2323-31, Aug. 2009
20. CBM fundamental research at the university of south carolina: A systematic approach to U.S. army rotorcraft CBM and the resulting tangible benefits
Blechertas, Vytautas (Condition-Based Maintenance Center, Department of Mechanical Engineering, University of South Carolina, Columbia, SC, United States); Bayoumi, Abdel; Goodman, Nicholas; Shah, Ronak; Shin, Yong-June Source: American Helicopter Society International - AHS International Condition Based Maintenance Specialists Meeting 2009, p 95-114, 2008, American Helicopter Society International - AHS International Condition Based Maintenance Specialists Meeting 2009
21. Signal processing on Brillouin scattering based distributed fibre sensors
Xiao, Shang-Hui (Dept. of Physics and Electron. Engineering, Yibin University, Sichuan, 644007, China); Li, Li Source: 2009 Symposium on Photonics and Optoelectronics, SOPO 2009, 2009, 2009 Symposium on Photonics and Optoelectronics, SOPO 2009
22. Title block: Reliability centered maintenance analysis of the UH-60 drive system
Lawler, Jason (Cargo and Special Projects Team Lead, U.S. Army AMRDEC Engineering Directorate, Redstone Arsenal, AL, United States); Potts, Lee; Hurst, Ellis Source: American Helicopter Society International - AHS International Condition Based Maintenance Specialists Meeting 2009, p 33-38, 2008, American Helicopter Society International - AHS International Condition Based Maintenance Specialists Meeting 2009
23. Cost effective implementation of HUMS, AME/ALE, and CBM+ through multi-platform collaboration on open source development
Maley, Scott (Program Office Automated Logistics Environment (ALE), Leads Naval Air Systems Command, PMA-275 / V-22, Patuxent River MD, United States); Cole, Dwayne; Davis, Mark; Stonebraker, Marc Source: Annual Forum Proceedings - AHS International, v 2, p 1709-1719, 2009, 65th Annual Forum Proceedings - AHS International
24. Home health telemonitoring system based on data mining
Jiang, Xianhai (School of Mechanical and Automotive Engineering, South China University of Technology (SCUT), Guangzhou 510640, China); Xie, Cunxi Source: Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009, v 2, p 431-434, 2009, Proceedings - 2009 International Forum on Information Technology and Applications, IFITA 2009
25. Design of processing and display system for intelligent structure characteristic data based on LabVIEW
Lang, Li-Ying (Hebei University of Engineering, Handan, Hebei Province, 056038, China); Shen, Lan Source: Proceedings of 2009 4th International Conference on Computer Science and Education, ICCSE 2009, p 604-607, 2009, Proceedings of 2009 4th International Conference on Computer Science and Education, ICCSE 2009

Review

2009-11-11T20:41:00.000-08:00

1. Impact of power control in wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) for railroad health monitoring
Tan, Hwee-Pink (Networking Protocols Department, Institute for Infocomm Research, Singapore); Lee, Pius W. Q.; Seah, Winston K. G.; Eu, Zhi Ang Source: Proceedings - International Conference on Advanced Information Networking and Applications, AINA, p 804-809, 2009, Proceedings - 2009 International Conference on Advanced Information Networking and Applications Workshops, WAINA 2009
2. Rotations in a shear-beam model of a seven-story building caused by nonlinear waves during earthquake excitation
Gicev, V. (Rudarsko-geoloski fakultet, Slip, Macedonia); Trifunac, M.D. Source: Structural Control and Health Monitoring, v 16, n 4, p 460-82, June 2009
3. Field measurement on wind characteristic and buffeting response of the Runyang suspension bridge during typhoon Matsa
Wang Hao (Coll. of Civil Eng., Southeast Univ., Nanjing, China); Li AiQun; Guo Tong; Xie Jing Source: Science in China Series E: Technological Sciences, v 52, n 5, p 1354-62, May 2009
4. Orthogonal frequency coded SAW sensors for aerospace SHM applications
Wilson, W.C. (Virginia Commonwealth Univ., Richmond, VA, USA); Malocha, D.C.; Kozlovski, N.; Gallagher, D.R.; Fisher, B.; Pavlina, J.; Saldanha, N.; Puccio, D.; Atkinson, G.M. Source: IEEE Sensors Journal, v 9, n 11, p 1546-56, Nov. 2009
5. Remote sensing for bridge health monitoring
Liu, Wanqiu (Department of Civil and Environmental Eng., UNCC, 9201 University City Boulevard, Charlotte, NC 28223-0001, United States); Chen, She-Een; Hauser, Edd Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7456, 2009, Atmospheric and Environmental Remote Sensing Data Processing and Utilization V: Readiness for GEOSS III
6. Upper bound H∞ and H2 control for collocated structural systems
Hiramoto, K. (Dept. of Mech. Eng., Akita Univ., Akita, Japan); Grigoriadis, K. Source: Structural Control and Health Monitoring, v 16, n 4, p 425-40, June 2009
8. ERA modal identification method for hydraulic structures based on order determination and noise reduction of singular entropy
Lian JiJian (Sch. of Civil Eng., Tianjin Univ., Tianjin, China); Li HuoKun; Zhang JianWei Source: Science in China Series E: Technological Sciences, v 52, n 2, p 400-12, Feb. 2009
9. Condition monitoring and diagnosis for subsea control systems. A subsystem prototype
Altamiranda, E. (Subsea Control Syst. Eng. Dept., VetcoGray a GE Oil & Gas Bus., Stavanger, Norway); Kiaer, L.; Hu, X. Source: OCEANS 2009-EUROPE (OCEANS), p 6 pp., 2009
10. Blast analysis of concrete arch structures for FRP retrofitting design
Nam, Jin-Won (Department of Civil and Environmental Engineering, University of California, Davis One Shield Ave., Davis, CA 95616, United States); Kim, Ho-Jin; Yi, Na-Hyun; Kim, In-Soon; Kim, Jang-Ho Jay; Choi, Hyung-Jin Source: Computers and Concrete, v 6, n 4, p 305-318, August 2009

11. Vibration signature analysis of high-speed unbalanced rotors supported by rolling-element bearings due to off-sized rolling elements
Upadhyay, Sanjay H. (Mechanical and Industrial Engineering Department, Indian Institute of Technology, Roorkee, India); Harsha, Suraj P.; Jain, S.C. Source: International Journal of Acoustics and Vibrations, v 14, n 3, p 163-171, September 2009

13. A novel method of searching appropriate ranges of base isolation design parameters through entropy-based classification
Pei Chiung Huang (Dept. of Civil Eng., Nat. Chung Hsing Univ., Taichung, Taiwan); Shiuan Wan; Jia Yih Yen Source: Structural Control and Health Monitoring, v 16, n 4, p 385-405, June 2009

14. Simultaneous state estimation and parameter tuning in a shear building with a magneto-rheological damper
Jimenez-Fabian, R. (Inst. de Ing., Univ. Nac. Autonoma de Mexico Circuito Exterior, Mexico City, Mexico); Alvarez-Icaza, L. Source: Structural Control and Health Monitoring, v 16, n 4, p 483-502, June 2009

15. Hydrogen energy demonstration plant in Patagonia: description and safety issues
Aprea, J.L. (CNEA (Argentine Atomic Energy Comm.), Comahue Univ., Neuquen, Argentina) Source: International Journal of Hydrogen Energy, v 34, n 10, p 4684-91, May 2009

Review

2009-11-01T20:24:00.000-08:00

1. Dual scale structural health monitoring system combining FBG sensors and laser scanning
Lima, Hugo F. (I3N-Aveiro, Physics Department, University of Aveiro, 381-193 Aveiro, Portugal); Domingues, M. Fátima; Nogueira, Rogério N.; André, Paulo; Pinto, João L. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7391, 2009, O3A: Optics for Arts, Architecture, and Archaeology II
2. Feature extraction and selection from vibration measurements for structural health monitoring
Toivola, Janne (Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, Espoo, TKK FI-02015, Finland); Hollmén, Jaakko Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 5772 LCNS, p 213-224, 2009, Advances in Intelligent Data Analysis VIII - 8th International Symposium on Intelligent Data Analysis, IDA 2009, Proceedings
3. WSNs for structural health monitoring of historical buildings
Anastasi, Giuseppe (Dept. of Information Engineering, University of Pisa, Via Diotisalvi, 2, 56122 Pisa, Italy); Lo Re, Giuseppe; Ortolani, Marco Source: Proceedings - 2009 2nd Conference on Human System Interactions, HSI '09, p 574-579, 2009, Proceedings - 2009 2nd Conference on Human System Interactions, HSI '09
4. Feature extraction and sensor fusion for ultrasonic structural health monitoring under changing environmental conditions
Yinghui Lu (Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA); Michaels, J.E. Source: IEEE Sensors Journal, v 9, n 11, p 1462-71, Nov. 2009
5. Optimal planning of structural performance monitoring based on reliability importance assessment
Kim, Sunyong (Department of Civil and Environmental Engineering, ATLSS Center, Lehigh University, 117 ATLSS Dr, Bethlehem, PA 18015-4729, United States); Frangopol, Dan M. Source: Probabilistic Engineering Mechanics, v 25, n 1, p 86-98, January 2010
6. Vertical displacement measurements for bridges using optical fiber sensors and CCD cameras a preliminary study
Chan, T.H.T. (Sch. of Urban Dev., Queensland Univ. of Technol., Brisbane, QLD, Australia); Ashebo, D.B.; Tarn, H.Y.; Yu, Y.; Chan, T.F.; Lee, P.C.; Gracia, E.P. Source: Structural Health Monitoring, v 8, n 3, p 243-9, May 2009
7. On selection of data fusion schemes for structural damage evaluation
Zhongqing Su (Dept. of Mech. Eng., Hong Kong Polytech. Univ., Kowloon, China); Xiaoming Wang; Li Cheng; Long Yu; Zhiping Chen Source: Structural Health Monitoring, v 8, n 3, p 223-41, May 2009
8. Embedded algorithms within an FPGA to classify nonlinear single-degree-of-freedom systems
Jones, J.D. (Sch. of Electr. & Comput. Eng., Univ. of Oklahoma, Norman, OK, USA); Jin-Song Pei Source: IEEE Sensors Journal, v 9, n 11, p 1486-93, Nov. 2009
9. Implementation of a long-term bridge weigh-in-motion system for a steel girder bridge in the interstate highway system
Cardini, A.J. (AECOM Transportation, Boston, MA 02110, United States); Dewolf, John T. Source: Journal of Bridge Engineering, v 14, n 6, p 418-423, 2009
10. Mobile health monitoring for the elderly: Designing for diversity
Lorenz, Andreas (Fraunhofer FIT, Schloss Birlinghoven, D-53754 St. Augustin, Germany); Oppermann, Reinhard Source: Pervasive and Mobile Computing, v 5, n 5, p 478-495, October 2009
11. Self-diagnosis of smart structures based on dynamical properties
Fritzen, C.-P. (Dept. of Mech. Eng., Univ. of Siegen, Siegen, Germany); Kraemer, P. Source: Mechanical Systems and Signal Processing, v 23, n 6, p 1830-45, Aug. 2009
12. Damage identification by multi-model updating in the modal and in the time domain
Link, M. (Inst. of Statics & Struct. Dynamics, Univ. of Kassel, Kassel, Germany); Weiland, M. Source: Mechanical Systems and Signal Processing, v 23, n 6, p 1734-46, Aug. 2009
13. Fatigue reliability assessment of retrofitted steel bridges integrating monitored data
Liu, Ming (Department of Civil and Environmental Engineering, Advanced Technology for Large Structural Systems (ATLSS) Center, Lehigh University, Bethlehem, PA 18015-4729, United States); Frangopol, Dan M.; Kwon, Kihyon Source: Structural Safety, v 32, n 1, p 77-89, January 2010
14. Harsh environment silicon carbide sensors for health and performance monitoring of aerospace systems: a review
Senesky, D.G. (Univ. of California, Berkeley, CA, USA); Jamshidi, B.; Kan Bun Cheng; Pisano, A.P. Source: IEEE Sensors Journal, v 9, n 11, p 1472-8, Nov. 2009
15. A method for the localization of damage in a CFRP plate using damping
Montalvao, D. (Dept. of Mech. Eng., Escola Super. de Tecnol. de Setubal, Setubal, Portugal); Ribeiro, A.M.R.; Duarte-Silva, J. Source: Mechanical Systems and Signal Processing, v 23, n 6, p 1846-54, Aug. 2009
16. Wireless sensor networks for strain monitoring during steel bridges launching
Chacon, R. (Constr. Eng. Dept., Univ. Politec. de Catalunya UPC, Barcelona, Spain); Guzman, F.; Mirambell, E.; Real, E.; Onate, E. Source: Structural Health Monitoring, v 8, n 3, p 195-205, May 2009
17. Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model
Kijsanayotin, B. (Bur. of Policy & Strategy, Minist. of Public Health, Nonthaburi, Thailand); Pannarunothai, S.; Speedie, S.M. Source: International Journal of Medical Informatics, v 78, n 6, p 404-16, June 2009
18. Er3+-activated photonic structures fabricated by sol-gel and rf-sputtering techniques
Ferrari, M. (CNR-IFN, CSMFO Lab., via alla Cascata 56/C, Povo, 38123 Trento, Italy); Alombert-Goget, G.; Armellini, C.; Berneschi, S.; Bhaktha, S.N.B.; Boulard, B.; Brenci, M.; Chiappini, A.; Chiasera, A.; Duverger-Arfuso, C.; Féron, P.; Gonçalves, R.R.; Jestin, Y.; Minati, L.; Moser, E.; Nunzi Conti, G.; Pelli, S.; Rao, D.N.; Retoux, R.; Righini, G.C.; Speranza, G. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7366, 2009, Photonic Materials, Devices, and Applications III
19. Antenna/sensor multifunctional composites for the wireless detection of damage
Matsuzaki, Ryosuke (Department of Mechanical Sciences and Engineering, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo, 152-8552, Japan); Melnykowycz, Mark; Todoroki, Akira Source: Composites Science and Technology, v 69, n 15-16, p 2507-2513, December 2009
20. Localized damage detection of structures subject to multiple ambient excitations using two distance measures for autoregressive models
Haitao Zheng (Dept. of Syst. Design Eng., Keio Univ., Yokohama, Japan); Mita, A. Source: Structural Health Monitoring, v 8, n 3, p 207-22, May 2009
21. Mobile agent technology for distributed sensing and actuation systems
Chen, Bo (Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, Houghton, MI 49931, United States) Source: 2008 Proceedings of the ASME Dynamic Systems and Control Conference, DSCC 2008, n PART B, p 1479-1485, 2009, 2008 Proceedings of the ASME Dynamic Systems and Control Conference, DSCC 2008
22. Providing real-time indoor air analytical results at a medical facility prior and subsequent to a chlorine dioxide fumigation using a Trace Atmospheric Gas Analyzer (TAGA) mounted on a moveable platform
Mickunas, David B. (US Environmental Protection Agency/Environmental Response Team, Mail Code E343-04, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, United States); Blaze, Stephen L.; Wood, J.; Thompson, S.; Weeks, W. Source: Air and Waste Management Association: Symposium on Air Quality Measurement Methods and Technology 2008, p 535-539, 2008, Air and Waste Management Association: Symposium on Air Quality Measurement Methods and Technology 2008
23. Cache-aware optimization of BAN applications
Ju, Lei (Department of Computer Science, National University of Singapore, Singapore, Singapore); Liang, Yun; Chakraborty, Samarjit; Mitra, Tulika; Roychoudhury, Abhik Source: Design Automation for Embedded Systems, v 13, n 3, p 159-178, September 2009
24. Effects of pH on the rieske protein from thermus thermophilus: A spectroscopic and structural analysis
Konkle, Mary E. (Department of Chemistry, Trinity University, One Trinity Place, San Antonio, TX 78212, United States); Muellner, Sarah K.; Schwander, Anika L.; Dicus, Michelle M.; Pokhrel, Ravi; Britt, R. David; Taylor, Alexander B.; Hunsicker-Wang, Laura M. Source: Biochemistry, v 48, n 41, p 9848-9857, October 20, 2009
25. Using field analytics to resolve vapor intrusion issues
Mickunas, David B. (US Environmental Protection Agency, Environmental Response Team, Mail Code E343-04, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, United States); Blaze, Stephen L.; Wood, J.; Thompson, S.; McCall, D.; Shields, C.; Weeks, W.; Magan, R. Source: Air and Waste Management Association: Symposium on Air Quality Measurement Methods and Technology 2008, p 103-107, 2008, Air and Waste Management Association: Symposium on Air Quality Measurement Methods and Technology 2008

Review

2009-10-25T21:11:00.000-07:00

1. Real-time system identification of a nonlinear four-story steel frame structure - application to structural health monitoring
Hann, C.E. (Dept. of Mech. Eng., Univ. of Canterbury, Christchurch, New Zealand); Singh-Levett, I.; Deam, B.L.; Mander, J.B.; Chase, J.G. Source: IEEE Sensors Journal, v 9, n 11, p 1339-46, Nov. 2009

2. An integrated sensing/communication architecture for structural health monitoring
Merlino, P. (Dipt. di Ing. Elettr., Gestionale e Meccanica, Univ. of Udine, Udine, Italy); Abramo, A. Source: IEEE Sensors Journal, v 9, n 11, p 1397-404, Nov. 2009

3. Structural health monitoring of bridges using acoustic emission technology
Tan, A.C.C. (Fac. of Built Environ. & Eng., Queensland Univ. of Technol., Brisbane, QLD, Australia); Kaphle, M.; Thambiratnam, D. Source: 2009 8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009), p 839-43, 2009

4. The development of robust structural health monitoring sensors utilizing TRIP steel
Bemont, C.P. (Univ. of KwaZulu-Natal, Durban, South Africa) Source: IEEE Sensors Journal, v 9, n 11, p 1449-55, Nov. 2009

5. Frequency domain based damage index for structural health monitoring
Giridhara, G. (Department of Mechanical Engineering, BMS College of Engineering, Bangalore 560019, India); Gopalakrishnan, S. Source: SDHM Structural Durability and Health Monitoring, v 5, n 1, p 1-31, 2009

6. Self-powered sensors for monitoring of highway bridges
Sazonov, E. (Dept. of Electr. & Comput. Eng., Clarkson Univ., Potsdam, NY, USA); Haodong Li; Curry, D.; Pillay, P. Source: IEEE Sensors Journal, v 9, n 11, p 1422-9, Nov. 2009

7. Fault-mode classification for health monitoring of electronics subjected to drop and shock
Lall, Pradeep (Auburn University, Department of Mechanical Engineering, NSF Center for Advanced Vehicle and Extreme Environment Electronics (CAVE3), Auburn, AL 36849, United States); Gupta, Prashant; Panchagade, Dhananjay; Angral, Arjun Source: Proceedings - Electronic Components and Technology Conference, p 668-681, 2009, 2009 Proceedings 59th Electronic Components and Technology Conference, ECTC 2009

8. Real time damage state estimation and condition based residual useful life estimation of a metallic specimen under biaxial loading
Mohanty, S. (Department of Mechanical and Aerospace Engineering, ASU, Tempe, AZ, United States); Chattopadhyay, A.; Wei, J.; Peralta, P. Source: SDHM Structural Durability and Health Monitoring, v 5, n 1, p 33-55, 2009

9. Highway bridge assessment using an adaptive real-time wireless sensor network
Whelan, M.J. (Clarkson Univ., Potsdam, NY, USA); Gangone, M.V.; Janoyan, K.D. Source: IEEE Sensors Journal, v 9, n 11, p 1405-13, Nov. 2009

10. Sensibility study for the near-field sub-band beamforming method for damage detection in bridges
Medda, Alessio (Department of Electrical and Computer Engineering, Florida State University); De Brunner, Victor Source: Conference Record - Asilomar Conference on Signals, Systems and Computers, p 398-401, 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008

11. Sensitivity of Eigen value to damage and its identification
Raghuprasad, B.K. (Indian Inst. of Sci., Bangalore, India); Lakshmanan, N.; Gopalakrishnan, N.; Muthumani, K. Source: Structural Durability & Health Monitoring, v 4, n 3, p 117-44, 2008

12. 3D integration technologies for ceramic substrates in a SHM application
Hildebrandt, S. (Electron. Packaging Lab., Dresden Univ. of Technol., Dresden, Germany); Wolter, K.-J. Source: 2009 European Microelectronics and Packaging Conference (EMPC), p 5 pp., 2009

13. Research on delamination monitoring for composite structures based on HHGA-WNN
Shi-jie Zheng (Aeronaut. Sci. Key Lab. of Smart Mater. & Struct., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China); Zheng-qiang Li; Hong-tao Wang Source: Applied Soft Computing, v 9, n 3, p 918-23, June 2009

14. RF cavity passive wireless sensors with time-domain gating-based interrogation for SHM of civil structures
Thomson, D.J. (Dept. of Electr. & Comput. Eng., Univ. of Manitoba, Winnipeg, MB, Canada); Card, D.; Bridges, G.E. Source: IEEE Sensors Journal, v 9, n 11, p 1430-8, Nov. 2009

15. Implementation and evaluation of a ubiquitous health monitoring system
Yamazaki, Akira (Department of Informatics, Graduate School of Science And Engineering, Yamagata University, Japan); Koyama, Akio; Arai, Junpei; Barolli, Leonard Source: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009, p 367-374, 2009, Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009

16. Optimizing energy-latency trade-off in sensor networks with controlled mobility
Sugihara, Ryo (CSE Department, University of California, San Diego, San Diego, CA 92093, United States); Gupta, Rajesh K. Source: Proceedings - IEEE INFOCOM, p 2566-2570, 2009, IEEE INFOCOM 2009 - The 28th Conference on Computer Communications

17. A survey of recent physiological monitoring and transmission to support the service of critical care
McGregor, C. (Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada); Smith, K.P.; Eklund, J.M. Source: 2009 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS), p 7 pp., 2009

18. Dynamic finite element model updating of prestressed concrete continuous box-girder bridge
Lin, Xiankun (Institute of Vibration Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China); Zhang, Lingmi; Guo, Qintao; Zhang, Yufeng Source: Earthquake Engineering and Engineering Vibration, v 8, n 3, p 399-407, September 2009

19. Submillimeter crack detection with Brillouin-based fiber-optic sensors
Ravet, F. (Omnisens SA, Morges, Switzerland); Briffod, F.; Glisic, B.; Nikles, M.; Inaudi, D. Source: IEEE Sensors Journal, v 9, n 11, p 1391-6, Nov. 2009

20. Case studies on vibration based damage identification: multi-criteria approach
Chan, T.H.T. (Fac. of Built Environ. & Eng., Queensland Univ. of Technol., Brisbane, QLD, Australia); Shih, H.W.; Thambiratnam, D.P. Source: 2009 8th International Conference on Reliability, Maintainability and Safety (ICRMS 2009), p 1242-7, 2009

21. A survey of physiological monitoring data models to support the service of critical care
McGregor, C. (Inst. of Technol., Univ. of Ontario, Oshawa, ON, Canada); Smith, K.P. Source: 2009 33rd Annual IEEE International Computer Software and Applications Conference (COMPSAC 2009), p 104-9, 2009

22. Fatigue analysis of spot-welded joint for automative structures
Rahman, M.M. (Fac. of Mech. Eng., Univ. Malaysia Pahang, Kuantan, Malaysia); Arrifin, A.K.; Nor, M.J.M.; Abdullah, S. Source: Structural Durability & Health Monitoring, v 4, n 3, p 173-80, 2008

23. Brillouin spectral response depending on strain non-uniformity within centimeter spatial resolution and its application to internal damage detection in large-scale composite structures
Minakuchi, Shu (Department of Advanced Energy, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8561, Japan); Mizutani, Tadahito; Tsukamoto, Haruka; Nishio, Mayuko; Okabe, Yoji; Takeda, Nobuo Source: SDHM Structural Durability and Health Monitoring, v 4, n 4, p 199-219, 2008

24. Evaluation of concrete penetration depth under impact loading employing empirical formulae
Murthy, A. Rama Chandra (Structural Engineering Research Centre, CSIR Campus, Taramani, Chennai-600 113, India); Palani, G.S.; Iyer, Nagesh R. Source: SDHM Structural Durability and Health Monitoring, v 4, n 4, p 221-229, 2008

25. FE analysis of a notched cylinder under multiaxial cyclic loading using the multilayer model of Besseling
Savaidis, G. (Aristotle Univ. Thessaloniki, Thessaloniki, Greece); Pitatzis, N.; Savaidis, A.; Zhang, C. Source: Structural Durability & Health Monitoring, v 4, n 3, p 145-60, 2008

review

2009-10-18T22:02:00.001-07:00

1. Periodic seismic performance evaluation of highway bridges using structural health monitoring system
Jin-Hak Yi (Coastal Eng. & Ocean Energy Res. Dept., Korea Ocean R&D Inst., Ansan, South Korea); Dookie Kim; Fengt, M.Q. Source: Structural Engineering and Mechanics, v 31, n 5, p 527-44, 30 March 2009

2. Force sensor system for structural health monitoring using passive RFID tags
Ikemoto, Yusuke (Research into Artifacts, Center for Engineering, University of Tokyo, Kashiwa-shi, Chiba, Japan); Suzuki, Shingo; Okamoto, Hiroyuki; Murakami, Hiroki; Asama, Hajime; Morishita, Soichiro; Mishima, Taketoshi; Lin, Xin; Itoh, Hideo Source: Sensor Review, v 29, n 2, p 127-136, 2009

3. Bio-inspired sensor skins for structural health monitoring
Tata, U. (Dept. of Mech. & Aerosp. Eng., Univ. of Texas at Arlington, Arlington, TX, USA); Deshmukh, S.; Chiao, J.C.; Carter, R.; Huang, H. Source: Smart Materials and Structures, v 18, n 10, p 104026 (8 pp.), Oct. 2009

4. A new impedance measurement system for PZT-based structural health monitoring
Baptista, F.G. (Dept. of Electr. Eng., Sao Paulo State Univ. (UNESP), Ilha Solteira, Brazil); Filho, J.V. Source: IEEE Transactions on Instrumentation and Measurement, v 58, n 10, p 3602-8, Oct. 2009

5. Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge
Whelan, Matthew J. (Clarkson University, Department of Civil and Environmental Engineering, Potsdam, NY 13699-5712, United States); Gangone, Michael V.; Janoyan, Kerop D.; Jha, Ratneshwar Source: Engineering Structures, v 31, n 10, p 2224-2235, October 2009

6. Ayushman: A secure, usable pervasive health monitoring system
Venkatasubramanian, K. (IMPACT Lab., School of Computing and Informatics, Arizona State University, Tempe, AZ 85287, United States); Gupta, S.K. Source: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, HealthNet'08, 2008, Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, HealthNet'08

7. Averaged acoustic emission events for accurate damage localization
Ince, N.F. (Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States); Kao, Chu-Shu; Kaveh, M.; Tewfik, A.; Labuz, J.F. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, p 2201-2204, 2009, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009

8. Health monitoring using support vector classification on an auxiliary power unit
Vieira, Fábio Manzoni (EMBRAER, Empresa Brasileira de Aeronáutica S.A., Av. Brigadeiro Faria Lima, 2170, Putim 12227-901 - São José dos Campos - SP, Brazil); Bizarria, Cintia De Oliveira; Nascimento Jr., Cairo Lúcio; Fitzgibbon, Kevin Theodore Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

9. Remote health monitoring increases reliability and condition-based maintenance
Green, Bruce (GBS Group, Bldg. 3300 South, 397 Little Neck Rd., Virginia Beach, VA 23452, United States); Hull, George; Hurtado, Juan; Harvill, Mark Source: American Society of Mechanical Engineers, Rail Transportation Division (Publication) RTD, p 143-148, 2009, 2008 Proceedings of the ASME Rail Transportation Division Fall Conference

10. The application of 1-3 cement-based piezoelectric transducers in active and passive health monitoring for concrete structures
Lei Qin (Sch. of Civil & Archit. Eng., Univ. of Jinan, Jinan, China); Shifeng Huang; Xin Cheng; Youyuan Lu; Zongjin Li Source: Smart Materials and Structures, v 18, n 9, p 095018 (8 pp.), Sept. 2009

11. Wireless and embedded carbon nanotube networks for damage detection in concrete structures
Saafi, Mohamed (Department of Construction Engineering and Management, North Dakota State University, Fargo, ND 58108, United States) Source: Nanotechnology, v 20, n 39, 2009

12. Smart bridges under development with new federal grant
Source: Noise & Vibration Worldwide, v 40, n 3, p 10-11, March 2009

13. Sensor system selection for prognostics and health monitoring
Cheng, Shunfeng (Proanostics and Health Manaqement Lab); Azarian, Michael; Pechf, Michael Source: 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008, v 3, n PART B, p 1383-1389, 2009, 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008

14. Hierarchical fault diagnosis and health monitoring in multi-platform space systems
Barua, A. (Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G 1M8, Canada); Khorasani, K. Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

15. Aircraft flap and slat systems health monitoring using statistical process control techniques
Leão, Bruno P. (EMBRAER, Empresa Brasileira de Aeronáutica S.A., Av. Brigadeiro Faria Lima, 2170, Putim São José dos Campos, SP 12227-901, Brazil); Gomes, João P. P.; Galvão, Roberto K. H.; Yoneyama, Takashi Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

16. Diagnostic enhancements for air vehicle HUMS to increase prognostic system effectiveness
Patrick, Romano (Impact Technologies, LLC, 200 Canal View Blvd., Rochester, NY 14623, United States); Smith, Matthew J.; Zhang, Bin; Byington, Carl S.; Vachtsevanos, George J.; Del Rosario, Romeo Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

17. Damage identification by computational model updating
Link, Michael (Institute of Statics and Structural Dynamics, University of Kassel, Germany); Weiland, Matthias Source: 2008 Proceedings of the 9th Biennial Conference on Engineering Systems Design and Analysis, v 2, p 683-693, 2009, 2008 Proceedings of the 9th Biennial Conference on Engineering Systems Design and Analysis

18. Prognostics and health monitoring for an electro-hydraulic flight control actuator
Bizarria, Cintia De Oliveira (EMBRAER, Empresa Brasileira de Aeronáutica S.A., Av. Brigadeiro Faria Lima, 2170, Putim São José dos Campos, SP 12227-901, Brazil); Yoneyama, Takashi Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

19. Quality of Service consideration for the wireless telemedicine and e-health services
Zvikhachevskaya, Anna (Department of Communication Systems (InfoLab21), Lancaster University, Lancaster, United Kingdom); Markarian, Garik; Mihaylova, Lyudmila Source: IEEE Wireless Communications and Networking Conference, WCNC, 2009, 2009 IEEE Wireless Communications and Networking Conference, WCNC 2009 - Proceedings

20. Exploring early evaluation techniques of ambient health promoting devices in home environments of senior citizens living independently
Rege, Rajasee (School of Informatics, Indiana University, Bloomington, IN 47408, United States); Jung, Heekyoung; Hazelwood, William; Orlov, Greg; Connelly, Kay; Shankar, Kalpana Source: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, HealthNet'08, 2008, Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, HealthNet'08

21. Configuring and enhancing measurement systems for damage identification
Kripakaran, Prakash (Applied Computing and Mechanics Laboratory (IMAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 18, 1015 Lausanne, Switzerland); Smith, Ian. F.C. Source: Advanced Engineering Informatics, v 23, n 4, p 424-432, October 2009

22. Statistical pattern recognition and built-in reliability test for feature extraction and health monitoring of electronics under shock loads
Lall, Pradeep (Department of Mechanical Engineering, National Science Foundation's Center for Advanced Vehicle and Extreme Environment Electronics, Auburn University, Auburn, AL 36849, United States); Choudhary, Prakirti; Gupte, Sameep; Hofmeister, James Source: IEEE Transactions on Components and Packaging Technologies, v 32, n 3, p 600-616, 2009

23. Condition based maintenance of military ground vehicles
Rabeno, Eric (US Army Materiel Systems Analysis Activity (AMSAA), Bldg. 392 (AMSRD-AMS-LA), 392 Hopkins Road, Aberdeen Proving Ground, MD 21005-5071, United States); Bounds, Mark Source: IEEE Aerospace Conference Proceedings, 2009, 2009 IEEE Aerospace Conference

24. The AIV quick look and health monitoring system of the AGILE payload
Bulgarelli, Andrea (INAF, IASF Bologna, Italy); Gianotti, Fulvio; Trifoglio, Massimo; Di Cocco, Guido; Tavani, Marco; Marisaldi, Martino Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7011, 2008, Space Telescopes and Instrumentation 2008: Ultraviolet to Gamma Ray

25. A localized vibration response technique for damage detection in bridges
Medda, Alessio (Department of Electrical and Computer Engineering, FAMU-FSU College of Engineering, Florida State University); DeBrunner, Victor Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, p 1337-1340, 2009, 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009

Review

2009-10-18T21:57:00.000-07:00

1. Towards a practical structural health monitoring technology for patched cracks in aircraft structure
Baker, A. (Air Vehicles Div., Defence Sci. & Technol. Organ., Melbourne, VIC, Australia); Rajic, N.; Davis, C. Source: Composites Part A: Applied Science and Manufacturing, v 40, n 9, p 1340-52, Sept. 2009

2. Polymer optical fiber sensors for distributed strain measurement and application in structural health monitoring
Liehr, S. (BAM Fed. Inst. for Mater. Res. & Testing, Berlin, Germany); Lenke, P.; Wendt, M.; Krebber, K.; Seeger, M.; Thiele, E.; Metschies, H.; Gebreselassie, B.; Munich, J.C. Source: IEEE Sensors Journal, v 9, n 11, p 1330-8, Nov. 2009

3. Applicability of acoustic noise correlation for structural health monitoring in nondiffuse field conditions
Moulin, E. (Dept. of OAE, Univ. de Valenciennes et du Hainaut Cambresis, Valenciennes, France); Abou Leyla, N.; Assaad, J.; Grondel, S. Source: Applied Physics Letters, v 95, n 9, p 094104 (3 pp.), 31 Aug. 2009

4. Advanced active health monitoring system of liquid rocket engines
Qing, X.P. (Acellent Technol., Inc., Sunnyvale, CA, USA); Zhanjun Wu; Beard, S.; Fu-Kuo Chang Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7375, p 737506 (8 pp.), 2008

5. An experimental low-cost, low-data-rate rapid structural assessment network
Jren-Chit Chin (Purdue Univ., West Lafayette, IN, USA); Rautenberg, J.M.; Ma, C.Y.T.; Pujol, S.; Yau, D.K.Y. Source: IEEE Sensors Journal, v 9, n 11, p 1361-9, Nov. 2009

6. A trusted affective model approach to proactive health monitoring system
Chao, Xu (School of Computer Science and Technology, Tianjin University, Tianjin, China); Zhiyong, Feng Source: Proceedings - 2008 International Seminar on Future BioMedical Information Engineering, FBIE 2008, p 429-432, 2008, Proceedings - 2008 International Seminar on Future BioMedical Information Engineering, FBIE 2008

7. Use of wireless sensors for timber trestle railroad bridges health monitoring assessment
Moreu, Fernando (ESCA Consultants, Inc., 2008 Linview Ave., Urbana, IL); Nagayama, Tomonori Source: Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders, v 314, 2008, Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders

8. Necessity of the bridge health monitoring system to mitigate natural and man-made disasters
Sung-Pil Chang (Seoul Nat. Univ., Seoul, South Korea); Jaeyeol Yee; Jungwhee Lee Source: Structure and Infrastructure Engineering, v 5, n 3, p 173-97, June 2009

9. Damage detection on the joint of steel frame through high-frequency admittance signals
Dansheng Wang (Sch. of Civil Eng. & Mech., Huazhong Univ. of Sci. & Technol., Wuhan, China); Hongping Zhu; Huaqiang Zhou; Haiping Yang Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7375, p 73753J (6 pp.), 2008

10. EADS generic approach for operational aircraft health monitoring
Ariel, Jacques (EADS) Source: IET Seminar Digest, v 2008, n 12390, 2008, IET Seminar on Aircraft Health Management for New Operational and Enterprise Solutions

11. Fiber in the home: Broadband networks and optic distributed sensing
Liu, Baozhong (College of Computer Science and Engineering, Wuhan Institute of Technology, 693 ChuXiong Road, Wuhan, 430073, China); Peng, Meng; Wang, Wei; Liu, Boya Source: Proceedings - 2008 International Seminar on Future BioMedical Information Engineering, FBIE 2008, p 223-228, 2008, Proceedings - 2008 International Seminar on Future BioMedical Information Engineering, FBIE 2008

12. Plate-like structure health monitoring based on ultrasonic guided wave technology by using bonded piezoelectric ceramic wafers
Liu Zenghua (Coll. of Mech. Eng. & Appl. Electron. Technol., Beijing Univ. of Technol., Beijing, China); Zhao Jichen; He Cunfu; Wu Bin Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7375, p 73755S (6 pp.), 2008

13. Comparison of modal parameter identification algorithms based on shaking table model test data
Jianping Han (Res. Inst. of Struct. Eng. & Disaster Reduction, Tongji Univ., Shanghai, China); Xilin Lu; Feixing Wang Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7375, p 73750K (7 pp.), 2008

14. Baseline-free imaging method based on new pzt sensor arrangements
Qiang, Wang (Aeronautical Science Key Laboratory for Smart Materials and Structures, Nanjing University of Aeronautics and Astronautics, 29 Yu Dao Street, Nanjing 210016, China); Shenfang, Yuan Source: Journal of Intelligent Material Systems and Structures, v 20, n 14, p 1663-1673, 2009

15. Policy and management of medical devices for the public health care sector in Benin
Houngbo, P.Th. (Minist. of Health, Benin); Wilt, G.J.V.D.; Medenou, D.; Dakpanon, L.Y.; Bunders, J.; Ruitenberg, J. Source: IET Seminar Digest, v 2008, n 12213, 2008, 5th IET International Seminar on Appropriate Healthcare Technologies for Developing Countries (AHT 2008)

16. Influence of interface degradation on the performance of piezoelectric actuators
Lanzara, Giulia (Department of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305-4035, United States); Yoon, Youngki; Kim, Yujun; Chang, Fu-Kuo Source: Journal of Intelligent Material Systems and Structures, v 20, n 14, p 1699-1710, 2009

17. Remote sensing for bridge health monitoring
Wanqiu Liu (UNCC, Depart. of Civil & Environ. Eng., Charlotte, NC, USA); She-Een Chen; Hauser, E. Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7456, p 74560D (10 pp.), 2009

18. Sequential damage detection approaches for beams using time-modal features and artificial neural networks
Jae-Hyung Park (Dept. of Ocean Eng., Pukyong Nat. Univ., Busan, South Korea); Jeong-Tae Kim; Dong-Soo Hong; Duc-Duy Ho; Jin-Hak Yi Source: Journal of Sound and Vibration, v 323, n 1-2, p 451-74, 5 June 2009

19. Piezoelectric active-sensor diagnostics and validation using instantaneous baseline data
Overly, T.G. (Los Alamos Nat. Lab., Los Alamos, NM, USA); Gyuhae Park; Farinholt, K.M.; Farrar, C.R. Source: IEEE Sensors Journal, v 9, n 11, p 1414-21, Nov. 2009

20. Passively controlled MR damper in the benchmark structural control problem for seismically excited highway bridge
Pradono, Mulyo Harris (Center for Structural Strength Technology, Agency for the Assessment and Application of Technology, Puspiptek, Tangerang 15314, Indonesia); Iemura, Hirokazu; Igarashi, Akira; Toyooka, Akihiro; Kalantari, Afshin Source: Structural Control and Health Monitoring, v 16, n 6, p 626-638, October 2009, Special Issue: Part 2: Benchmark Structural Control Problem for Seismically Excited Highway Bridge

21. Research of cable tension sensor with difference structure based on magnetoelastic effect
Dedong Tang (Coll. of Electron. Inf. Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China); Shangzun Yuan; Xinping Yan; Pengfei Li Source: 2009 8th IEEE International Conference on Cognitive Informatics (ICCI), p 264-9, 2009

22. Inverse problem in damage detection
Ying Luo (Fac. of Sci., Jiangsu Univ., Zhenjiang, China); Zhongfang Li Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7375, p 73752Q (6 pp.), 2008

23. Service contracting for helicopter HUMS
Dollin, Andy Source: IET Seminar Digest, v 2008, n 12390, 2008, IET Seminar on Aircraft Health Management for New Operational and Enterprise Solutions

24. Robust direct adaptive controller for the nonlinear highway
Benchmark, Bridge (Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue W., Waterloo, ON N2L 3G1, Canada); Narasimhan, Sriram Source: Structural Control and Health Monitoring, v 16, n 6, p 599-612, October 2009, Special Issue: Part 2: Benchmark Structural Control Problem for Seismically Excited Highway Bridge

25. Design of smart refrigerator for ubiquitous healthcare
Bo-Rim Ryu (Seoul Women's Univ., Seoul, South Korea); Tae Soo Lee; Heung-Gyoon Ryu Source: IET Conference on Assisting Living 2009, p 4 pp., 2009

Review

2009-10-11T21:08:00.000-07:00

1. Application of smart 3-D laser scanner in structural health monitoring
Sumitro, S. (Smart Structures LLC, IL, United States); Matsuda, H.; Itoh, Y.; Nishimura, S. Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 566-567, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

2. Structural health monitoring of a concrete column subjected shake table excitations using smart aggregates
Liao, W.I. (National Taipei University of Technology, Taipei, Taiwan); Gu, H.; Olmi, C.; Song, G.; Mo, Y.L.; Loh, C.H. Source: Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, v 323, 2008, Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

3. Structural health monitoring of bridges in the State of Connecticut
Liu, C. (WSP-SELLS Inc., Briarcliff Manor, NY, USA); Olund, J.; Cardini, A.; D'Attilio, P.; Feldblum, E.; DeWolf, J. Source: Earthquake Engineering and Engineering Vibration, v 7, n 4, p 427-37, Dec. 2008

4. Structural health monitoring with smart NDE technology
Lee, Seung-Seok (Safety Metrology Group, Korea Research Institute of Standards and Science, Korea, Republic of); Kwon, Il-Bum; Yoon, Dong-Jin Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 17-20, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

5. Optimal experimental design for structural health monitoring applications
Lam, H.F. (City University of Hong Kong, Department of Building and Construction, Hong Kong, Hong Kong); Papadimitriou, C.; Ntotsios, E. Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 238, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

6. A new impedance measurement system for PZT-based structural health monitoring
Baptista, Fabricio Guimarães (Department of Electrical Engineering, São Paulo State University (UNESP), São Paulo 15385-000, Brazil); Filho, Jozue Vieira Source: IEEE Transactions on Instrumentation and Measurement, v 58, n 10, p 3602-3608, 2009

7. A circuit design for piezoelectric impedance-based health monitoring
Zhao, Zhou (North Dakota State University, Fargo, ND 58105-5285, United States); Wang, Shirui; You, Chao Source: Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, v 323, 2008, Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

8. Structural health monitoring with fiber optic sensors
Ansari, F. (Dept. of Civil & Mater. Eng., Smart Sensors, Univ. of Illinois at Chicago, Chicago, IL, USA) Source: Frontiers of Mechanical Engineering in China, v 4, n 2, p 103-10, June 2009

9. On optimal sensor placement for structural health monitoring by subspace approximation
Li, D.S. (Dept. of Civil Engineering, Dalian University of Technology, 116023, China); Wang, G.X.; Li, H.N.; Ren, L.; Song, G.B. Source: Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, v 323, 2008, Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

10. Structural integrity monitoring index for ship and offshore structures
De Leeuw, B. (Plant Asset Management, Houston, TX, United States); Brennan, F.P. Source: Proceedings of MARSTRUCT 2009, 2nd International Conference on Marine Structures-Analysis and Design of Marine Structures, p 261-265, 2009, Proceedings of MARSTRUCT 2009, 2nd International Conference on Marine Structures - Analysis and Design of Marine Structures

11. Adaptive quadratic sum squares error with unknown inputs for damage identification of structures
Yang, Jann N. (Department of Civil and Environmental Engineering, University of California, Irvine, CA, United States); Huang, Hongwei Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 243, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

12. Intelligent structures and systems: Dream, reality, and future
Huang, Shang-Lian (Key Laboratory for Optoelectronic Technology and System, Education Ministry of China, Chongqing University, Chongqing, China) Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 3-15, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

13. Structural monitoring and design verification of Akashi Kaikyo bridge
Katsuchi, H. (Yokohama National University, Yokohama, 240-8501, Japan); Yamada, H.; Kusuhara, S. Source: Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments, v 323, 2008, Earth and Space Conference 2008: Proceedings of the 11th Aerospace Division International Conference on Engineering, Science, Construction, and Operations in Challenging Environments

14. Suitability of nondestructive testing methods for structural health monitoring of civil infrastructures
Alampalli, Sreenivas (Bridge Evaluation Services Bureau, New York Department of Transportation, 50 Wolf Road, Albany, NY 12232); Ettouney, Mohammed Source: Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders, v 314, 2008, Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders

15. Research of smart materials and structures at NUAA
Qiu, Jinhao (Aeronautical Key Laboratory for Smart Materials and Structures, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China) Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 63-72, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

16. Utilization of strong motion accelerometers for real time monitoring of structural properties
Halling, Marvin W. (Utah State University, Logan, UT); Hansen, Zachary C.; Barr, Paul J.; Marchant, David Source: Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders, v 314, 2008, Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders

17. Distributed model updating in smart wireless monitoring systems
Zimmerman, Andrew (University of Michigan, Ann Arbor, MI); Lynch, Jerome P. Source: Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders, v 314, 2008, Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders

18. Long-term health monitoring of post-tensioning box girder bridges
Wang, Ming L. (University of Illinois-Chicago, Chicago, IL, United States) Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 41-48, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

19. Remote health monitoring adoption model based on artificial neural networks
Huang, Jui-Chen (Department of Health Business Administration, Hungkuang University, Taiwan) Source: Expert Systems with Applications, v 37, n 1, p 307-314, January 2010

20. Health monitoring systems for massive emergency situations
Smalls, Jordan; Wang, Yue; Li, Xi; Chen, Zehuang; Tang, K. Wendy Source: 2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009, 2009, 2009 IEEE Long Island Systems, Applications and Technology Conference, LISAT 2009

21. Bridge health monitoring system based on vibration measurements
Ntotsios, E. (Dept. of Mech. & Ind. Eng., Univ. of Thessaly, Volos, Greece); Papadimitriou, C.; Panetsos, P.; Karaiskos, G.; Perros, K.; Perdikaris, P. Source: Bulletin of Earthquake Engineering, v 7, n 2, p 469-83, May 2009

22. Long-term thermal performance of a CFRP-reinforced bridge deck
Bell, Erin Santini (University of New Hampshire, Kingsbury Hall, Durham, NH 03824); Sipple, Jesse; Yost, Joseph Source: Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders, v 314, 2008, Proceedings of the 2008 Structures Congress - Structures Congress 2008: Crossing the Borders

23. Experiment on dynamic response of fiber optic Fabry-Perot sensors and its application in structural health monitoring
Zhang, Wentao (Key Lab. of Structure Health Monitoring and Control of Hebei Province, Shijiazhuang Railway Institute, Shijiazhuang, Hebei, China); Dai, Jingyun; Sun, Baochen; Du, Yanliang Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 504, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

24. Damage monitoring and life predication from nondestructive evaluation to structural health monitoring
Tian, Gui Yun (School of Electrical, Electronic and Computer Engineering, Newcastle University, United Kingdom); Wilson, John; Zhang, Binqiang; Haitao Wang Source: Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST'07, p 159, 2008, Proceedings of the World Forum on Smart Materials and Smart Structures Technology, SMSST 07

25. Artificial immune pattern recognition for structure damage classification
Chen, Bo (Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 815 R.L. Smith Building, 1400 Townsend Dr., Houghton, MI 49931, United States); Zang, Chuanzhi Source: Computers and Structures, v 87, n 21-22, p 1394-1407, November 2009

2009-09-30T20:46:00.000-07:00

1. Wireless-zigbee strain gage sensor system for structural health monitoring
Ide, Hiroshi (Alpha STAR Electronic, Long beach, CA, United States); Abdi, Frank; Dang, Chau; Takahashi, Tatsuya; Sauer, Bruce; Miraj, Rashid Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7314, 2009, Photonics in the Transportation Industry: Auto to Aerospace II
2. Structural health monitoring robot using paired structured light
Hyun Myung (Dept. of Civil & Environ. Eng., KAIST, Daejeon, South Korea); Seungmok Lee; Bum-Joo Lee Source: 2009 IEEE International Symposium on Industrial Electronics (ISIE 2009), p 396-401, 2009
3. Scaled acoustics experiments and vibration prediction based structural health monitoring
Sarigul-Klijn, Nesrin (Department of Mechanical and Aeronautical Engineering, University of California at Davis, Davis, CA, United States); Lopez, Israel; Baek, Seung-Il Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 11, p 363-371, 2009, 2008 Proceedings of the ASME International Mechanical Engineering Congress and Exposition, IMECE 2008
4. Fibre-optic structural health monitoring in the energy industry
Ecke, W. (IPHT - Inst. of Photonic Technol., Jena, Germany); Willsch, R.; Bartelt, H. Source: 2009 14th OptoElectronics and Communications Conference (OECC), p 2 pp., 2009
5. Test-bed for the remote health monitoring system for bridge structures using FBG sensors
Lee, Chin-Hyung (Structural Engineering and Bridges Research Division, Infrastructure Research Department, Korea Institute of Construction Technology, 2311, Daewha-dong, Ilsanseo-gu, Goyang-si, Gyeonggi-do 411-712, Korea, Republic of); Park, Ki-Tae; Joo, Bong-Chul; Hwang, Yoon-Koog Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7362, 2009, Smart Sensors, Actuators, and MEMS IV
6. Modal identification and damage detection for structural health monitoring under ambient vibration environment
Yun, Gun Jin (The University of Akron, Department of Civil Engineering, 244 Sumner St., Akron, United States) Source: Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role, p 1181-1190, 2009, Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role
7. Title: Accounting for the impact of thermal loads in nondestructive bridge testing
Bell, Erin Santini (Department of Civil Engineering, University of New Hampshire, Durham, NH 03824, United States); Sipple, Jesse D. Source: Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role, p 406-415, 2009, Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role
8. Design and evaluation of optical fibre sensors in civil engineering applications for Structural Health Monitoring
Grattan, K.T.V. (Sch. of Eng. & Math. Sci., City Univ. London, Northampton, UK); Kerrouche, A.; Tong Sun; Grattan, S.K.T.; Taylor, S.E.; Basheer, P.A.M. Source: 2009 14th OptoElectronics and Communications Conference (OECC), p 2 pp., 2009
9. Structural health monitoring of bridges: Fundamentals, application case study and organizational considerations
Catbas, F.Necati (University of Central Florida, Orlando, FL, United States); Gul, Mustafa; Zaurin, Ricardo; Terrell, Thomas; Dere, Yunus; Ansley, Marcus H.; Frangopol, Dan M.; Grimmelsman, Kirk A. Source: Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role, p 108-118, 2009, Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role
10. Applicability of acoustic noise correlation for structural health monitoring in nondiffuse field conditions
Moulin, Emmanuel (Department of OAE, IEMN, Universit de Valenciennes et du Hainaut Cambrsis, Le Mont Houy, 59313 Valenciennes Cedex 9, France); Abou Leyla, Najib; Assaad, Jamal; Grondel, Sebastien Source: Applied Physics Letters, v 95, n 9, 2009
11. A semantic middleware for autonomic wireless sensor networks
Da Rocha, Atslands R. (Federal University of Ceará, km5 Estrada do Cedro, Quixadá CE, Brazil); Delicato, Flávia C.; De Souza, José Neuman; Gomes, Danielo G.; Pirmez, Luci Source: ACM International Conference Proceeding Series, v 389, p 19-25, 2009, Proceedings of the 2009 Workshop on Middleware for Ubiquitous and Pervasive Systems, WMUPS'09, Co-located with The 4th International Conference on COMmunication System softWAre and middlewaRE - COMSWA
12. Reference-free damage detection using instantaneous baseline measurements
Anton, Steven R. (Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States); Inman, Daniel J.; Park, Gyuhae Source: AIAA Journal, v 47, n 8, p 1952-1964, August 2009
13. Structural damage identification based on laser ultrasonic propagation imaging technology
Chiaa, Chen-Ciang (Department of Aerospace Engineering, Chonbuk National University, Duckjin-dong, Jeonju 561-756, Korea, Republic of); Janga, Si-Gwang; Lee, Jung-Ryul; Yoon, Dong-Jin Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7389, 2009, Optical Measurement Systems for Industrial Inspection VI
14. Application of a time-domain local identification methodology to compact analysis of continuous and complete structural response data
Nigbor, Robert L. (Department of Civil and Environmental Engineering, UCLA, Los Angeles, CA 90095, United States); Nayeri, Reza D.; Masri, Sami F.; Ghanem, Roger G. Source: Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role, p 1928-1937, 2009, Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role
15. Effect of bending dynamics of piezoelectric patch actuator on lamb wave displacement field
Huang, H. (University of Texas at Arlington, Arlington, TX, United States); Pamphile, T. Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 12, p 539-548, 2009, 2008 Proceedings of ASME International Mechanical Engineering Congress and Exposition, IMECE 2008
16. Fiber optic sensor applications in transportation infrastructure protection
Krohn, David (Light Wave Venture LLC); Nicholls, Paul Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7314, 2009, Photonics in the Transportation Industry: Auto to Aerospace II

17. A self-powered wireless health and environment monitoring system
Singh, Ajay (Autonomous Systems Laboratory, Department of Mechanical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794-2300, United States); Koomson, Vincent; Yu, Jaewook; Nejat, Goldie Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 2, p 511-519, 2009, 2008 Proceedings of ASME International Mechanical Engineering Congress and Exposition, IMECE 2008

18. Damage detection with spatially distributed 2D Continuous Wavelet Transform
Huang, Yi (CCG Veritas, Houston, TX 77072, United States); Meyer, D.; Nemat-Nasser, S. Source: Mechanics of Materials, v 41, n 10, p 1096-1107, October 2009

19. Prognostication and health monitoring of electronics in implantable biological systems
Lall, Pradeep (Auburn University, Department of Mechanical Engineering, NSF Center for Advanced Vehicle Electronics, Auburn, AL 36849, United States); Gupta, Prashant; Kulkarni, Manish; Panchagade, Dhananjay; Suhling, Jeff; Hofmeister, James Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 2, p 657-671, 2009, 2008 Proceedings of ASME International Mechanical Engineering Congress and Exposition, IMECE 2008

20. Experimental deployment and validation of a distributed SHM system using wireless sensor networks
Castaneda, Nestor E. (Department of Mechanical, Aerospace and Structural Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States); Dyke, Shirley; Lu, Chenyang; Sun, Fei; Hackmann, Greg Source: Structural Engineering and Mechanics, v 32, n 6, p 787-809, August 2009

21. Online condition monitoring of axial-flow turbomachinery blades using rotor-axial Eulerian laser Doppler vibrometry
Oberholster, A.J. (Dept. of Mech. & Aeronaut. Eng., Univ. of Pretoria, Pretoria, South Africa); Heyns, P.S. Source: Mechanical Systems and Signal Processing, v 23, n 5, p 1634-43, July 2009

22. New dissipative leapfrog finite difference scheme for elastodynamic simulation in noninertial frames
Kaul, Upender K. (NASA Ames Research Center, Moffett Field, CA 94035, United States) Source: AIAA Journal, v 47, n 8, p 1916-1925, August 2009

23. Realtime damage detection in buildings using filter based radial basis function network mapping
Contreras, Michael (Depts. of Civil and Env. Engr., Rice University, Houston, TX 77005, United States); Nagarajaiah, Satish; Narasimhan, Sriram Source: Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role, p 1097-1106, 2009, Proceedings of the 2009 Structures Congress - Don't Mess with Structural Engineers: Expanding Our Role

24. Analytical estimation of power harvested from a rotating helicopter blade
Warner, Grant M. (Howard University, Washington, DC, United States); Ogunlade, Olufemi; Lin, Ray Sing; Chaudhry, Zaffir; Wake, Brian Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 11, p 447-450, 2009, 2008 Proceedings of the ASME International Mechanical Engineering Congress and Exposition, IMECE 2008

25. Estimation of AE parameters for monitoring bearing in a lathe using multiple regression and GMDH
Sathish Kumar, K.M. (Department of Mechanical Engineering, BMS Institute of Technology, Bangalore Karnataka, India); Ravindra, H.V. Source: ASME International Mechanical Engineering Congress and Exposition, Proceedings, v 11, p 81-88, 2009, 2008 Proceedings of the ASME International Mechanical Engineering Congress and Exposition, IMECE 2008

2009-09-21T22:04:00.000-07:00

Urgent Opening for VerPro Bishnupur( Sr.Position)
We are pleased to inform you that we have an opening with our prestigious client VerPro in Bishnupur

Kindly let us know your interest in this position and also send us your update RESUME in WORD FORMAT immediately mentioning
1)present CTC
2)expected CTC
3)Notice period.
4)Willing to relocate to Bishnupur, WB, India

Experience:10+

The individual should be expert and take full responsibility for the following

1. Complete knowledge of all major SHM research labs and professors in India and preferably globally
2. Identifying the best partners in the eco-system for particular research needs in SHM
3. Establishing formal relationships – MoU/NDA/IPR sharing/etc. and connecting SHM researchers to appropriate groups
4. Maintaining friendly ties in the eco-system, while the researchers co-work with their corresponding professors
5. Handling sensitive / confidential information carefully
6. Regular status reports to VerPro
7. Excellent written and oral communication skills

Role: Open R&D management

Keyword: Managing University Relations

Qualification:
Ph.D / M.Tech / MS Computational Science/Engineering.

Exp: 10 +yrs

reference

2009-09-21T21:14:00.000-07:00

1. “Corrosion-enabled powering approach for structural health monitoring sensor networks” Ouellette, Scott A. (Department of Structural Engineering, University of California San Diego, 9500 Gilman Dr. 0085, San Diego, CA 92093-0085); Mascareñas, David D.L.; Todd, Michael D. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7288, 2009, Active and Passive Smart Structures and Integrated Systems 2009 Language: English
2. “Validation of HDOP measure for impact detection in sensor network-based structural health monitoring” Akopian, D. (Dept. of Electr. & Comput. Eng., Univ. of Texas at San Antonio, San Antonio, TX, USA); Melkonyan, A.; Chen, C.L.P. Source: IEEE Sensors Journal, v 9, n 9, p 1098-102, Sept. 2009
3. “Design and implementation of the structure health monitoring system for bridge based on wireless sensor network” Yin, An (Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China); Wang, Bingwen; Liu, Zhuo; Hu, Xiaoya Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v 5553 LNCS, n PART 3, p 915-922, 2009, Advances in Neural Networks - ISNN 2009 - 6th International Symposium on Neural Networks, ISNN 2009, Proceedings Language: English
4. “Structural damage detection using combined finite-element and model Lamb wave propagation parameters” Ratnam, C. (Dept. of Mech. Eng., Andhra Univ., Visakhapatnam, India); Ben, B.S.; Ben, B.A. Source: Proceedings of the Institution of Mechanical Engineers, Part C (Journal of Mechanical Engineering Science), v 223, n C3, p 769-77, March 2009
5. “Energy harvesting and wireless energy transmission for embedded sensor nodes” Farinholt, Kevin (Engineering Institute MS T001, Los Alamos National Laboratory, Los Alamos, NM 87544); Taylor, Stuart; Miller, Nathan; Sifuentes, Wilfredo; Moro, Erik; Park, Gyuhae; Farrar, Charles; Flynn, Eric; Mascarenas, David; Todd, Michael Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7288, 2009, Active and Passive Smart Structures and Integrated Systems 2009 Language: English
6. “Expert group neural networks for structural health diagnosis of Mau-Lo creek cable-stayed bridge” Lin, C.-C.J. (Nat. Center for Res. on Earthquake Eng., Taipei, Taiwan); Yu-Chi Sung Source: ICIC Express Letters, v 3, n 1, p 47-52, March 2009
7. “An oil condition monitoring technique to determine the optimal oil type and maintenance schedule” Mollazade, K. (Dept. of Agric. Machinery, Univ. of Tehran, Karaj, Iran); Ahmadi, H. Source: Structural Health Monitoring, v 8, n 4, p 331-9, July 2009
8. “Robust feature extraction for rapid classification of damage in composites” Coelho, Clyde K. (Department of Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ 85287-6106, United States); Reynolds, Whitney; Chattopadhyay, Aditi Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7286, 2009, Modeling, Signal Processing, and Control for Smart Structures 2009 Language: English
9. “Secure wireless collection and distribution of commercial airplane health data” Sampigethaya, Krishna (Department of Electrical Engineering, University of Washington, Seattle, WA, United States); Poovendran, Radha; Bushnell, Linda; Li, Mingyan; Robinson, Richard; Lintelman, Ott Source: IEEE Aerospace and Electronic Systems Magazine, v 24, n 7, p 14-20, July 2009 Language: English
10. “A decentralized Gauss-Seidel approach for in-network sparse signal recovery” Qing Ling (Dept. of ECE, Michigan Technol. Univ., Houghton, MI, USA); Zhi Tian Source: 2009 12th International Conference on Information Fusion (FUSION), p 380-7, 2009
11. “How can active hums safeguard towards the increasing complexity of avionics?” Melentis, J. (Vetronics Research Centre, University of Sussex, Brighton, BN1 9QT, United Kingdom); Ibarra, I.; Stipidis, E.; Charchalakis, P.; Ali, F.H. Source: IET Conference Publications, n 542 CP, 2008, 3rd IET International Conference on System Safety 2008 Language: English
12. “Active infrared thermographic inspection technique for elevated concrete structures using remote heating system” Kurita, Koichi (Department of Electrical Engineering, Kochi National College of Technology, 200-1 Monobe-Otsu, Nankoku, Kochi, 783-8508, Japan); Oyado, Michiaki; Tanaka, Hisashi; Tottori, Seiichi Source: Infrared Physics and Technology, v 52, n 5, p 208-213, September 2009 Language: English
13. “Energy harvesting utilizing single crystal PMN-PT material and application to a self-powered accelerometer” Song, H.J. (Dept. of Aerosp. Eng., Univ. of Maryland, College Park, MD, USA); Choi, Y.T.; Wang, G.; Wereley, N.M. Source: Journal of Mechanical Design, v 131, n 9, p 091008 (8 pp.), Sept. 2009
14. “Health forecast for aircraft based on adaptive MVGFM” Jianguo Cui (Automatization Coll., Shenyang Inst. of Aeronaut. Eng., Shenyang, China); Desheng Song; Ming Li; Changjun Xu; Peng Shi Source: 2009 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), p 821-4, 2009
15. “Design and study of fiber optic strain demodulation system based on LabVIEW” Lang Li-ying (Hebei Univ. of Eng., Handan, China); Shen Lan; Zhao Jian-jiao Source: 2009 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), p 288-91, 2009
16. “Acoustic emission techniques for early detection of bearing faults using LabVIEW” Elmaleeh, Mohammed A. A. (Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia); Saad, N. Source: Proceeding of the 5th International Symposium on Mechatronics and its Applications, ISMA 2008, 2008, Proceeding of the 5th International Symposium on Mechatronics and its Applications, ISMA 2008 Language: English
17. “Long-term effects of orthophosphate treatment on copper concentration” Schock, Michael R. (Division of Water Resources, US Environmental Protection Agency, 26 W. Martin Luther, Cincinnati, OH 45220); Sandvig, Anne M. Source: Journal / American Water Works Association, v 101, n 7, p 71-82+12, July 2009 Language: English
18. “Fiber-optic Bragg gratings as magnetic field-insensitive strain sensors for the surveillance of cryogenic devices” Latka, I. (Inst. of Photonic Technol. (IPHT), Jena, Germany); Ecke, W.; Hofer, B.; Habisreuther, T.; Willsch, R. Source: Cryogenics, v 49, n 9, p 490-6, Sept. 2009
19. “Power harvesting for railroad track safety enhancement using vertical track displacement” Nelson, Carl A. (Dept. of Mechanical Engineering, Univ. of Nebraska-Lincoln, N104 WSEC, Lincoln, NE 68588); Platt, Stephen R.; Hansena, Sean E.; Fateh, Mahmood Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7288, 2009, Active and Passive Smart Structures and Integrated Systems 2009 Language: English
20. “Damage detection in steel plates using artificial neural networks” Krishnan, R.P. (Sch. of Mechatron. Eng., Univ. Malaysia Perlis, Arau, Malaysia); Rahiman, M.H.F.; Yaacob, S.; Majid, M.S.A.; Paulraj, M.P. Source: 2009 International Conference on Control, Automation, Communication and Energy Conservation (INCACEC), p 4 pp., 2009
21. “Application of optical coherence tomography for imaging of scaffold structure and micro-flows characterization” Veksler, B. (Cranfield Health, Cranfield University, Cranfield, MK43 0AL, United Kingdom); Kobzev, E.; Bonesi, M.; Meglinski, I. Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7139, 2008, 1st Canterbury Workshop on Optical Coherence Tomography and Adaptive Optics Language: English
22. “ Energy harvesting - Small scale energy production from ambient sources” Yeatman, Eric M. (Imperial College London, Exhibition Rd., London SW7 2AZ, United Kingdom) Source: Proceedings of SPIE - The International Society for Optical Engineering, v 7288, 2009, Active and Passive Smart Structures and Integrated Systems 2009 Language: English

2009-09-13T21:48:00.001-07:00

1. “Structural integrity monitoring index (SIMdex): A methodology for assessing structural health monitoring technologies” De Leeuw, B. (Plant Asset Management, Enterprise House, Ocean Village, Southampton, United Kingdom); Brennan, F.P. Source: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, v 223, n 5, p 515-523, August 1, 2009
2. ”Automated impedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects” Ki-Young Koo (Dept. of Civil & Environ. Eng., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea); Seunghee Park; Jong-Jae Lee; Chung-Bang Yun Source: Journal of Intelligent Material Systems and Structures, v 20, n 4, p 367-77, March 2009
3. “Structural health monitoring of rods based on natural frequency and antiresonant frequency measurements” Dilena, M. (Dipt. di Georisorse e Territorio, Universitd degli Studi di Udine, Udine, Italy); Morassi, A. Source: Structural Health Monitoring, v 8, n 2, p 149-73, March 2009
4. “Bridge system performance assessment from structural health monitoring: A case study” Liu, Ming (Lehigh Univ., Dept. of Civil and Environmental Engineering, ATLSS Center, 117 ATLSS Dr., Bethlehem, PA 18015-4729, United States); Frangopol, Dan M.; Kim, Sunyong Source: Journal of Structural Engineering, v 135, n 6, p 733-742, 2009
5. “Dual scale structural health monitoring system combining FBG sensors and laser scanning” Lima, H.F. (Phys. Dept., Univ. of Aveiro, Aveiro, Portugal); Domingues, M.F.; Nogueira, R.N.; Andre, P.; Pinto, J.L. Source: Proceedings of the SPIE - The International Society for Optical Engineering, v 7391, p 73910O (8 pp.), 2009
6. “Use of sensor systems for remote monitoring of corrosion and structural health: Case studies” Hill, D. (Det Norske Veritas Research and Innovation, 5777 Frantz Rd., Dublin, OH 43017); Marion, S.; Ayello, F.; Cunci, L.; Sridhar, N. Source: NACE - International Corrosion Conference Series, 2009, Corrosion 2009
7. “Lessons learned from health monitoring of rocket motors” Buswell, Jim (Newton Consultancy (Apple Fall) Ltd); Fall, Apple Source: 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2005, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit
8. “Performance evaluation and reliable implementation of data transmission for wireless sensors on rotating mechanical structures” Lei Tang (Dept. of Mech. Eng., Clemson Univ., Clemson, SC, USA); Kuang-Ching Wang; Yong Huang Source: Structural Health Monitoring, v 8, n 2, p 113-24, March 2009
9. “Optimal placement of sensors for sub-surface fatigue crack monitoring” Teo, Y.H. (Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Vic. 3168, Australia); Chiu, W.K.; Chang, F.K.; Rajic, N. Source: Theoretical and Applied Fracture Mechanics, v 52, n 1, p 40-49, August 2009
10. “A monolithic interdigitated PVDF transducer for lamb wave inspection” Hua Gu (Caterpillar Production Syst. Div., Caterpillar Inc., Mossville, IL, USA); Wang, M.L. Source: Structural Health Monitoring, v 8, n 2, p 137-48, March 2009
11. “Structural damage detection using auxiliary particle filtering method” Songtao Xue (Sch. of Sci. & Eng., Dept. of Archit., Kinki Univ., Higashi-Osaka, Japan); Hesheng Tang; Qiang Xie Source: Structural Health Monitoring, v 8, n 2, p 101-12, March 2009
12. “Acoustic emission for monitoring aircraft structures” Holford, K.M. (Cardiff School of Engineering, Cardiff University, Queen's Building, Parade, Cardiff, CF24 3AA, United Kingdom); Pullin, R.; Evans, S.L.; Eaton, M.J.; Hensman, J.; Worden, K. Source: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, v 223, n 5, p 525-532, August 1, 2009
13. “Minimal-sensing, passive force identification techniques for a composite structural missile component” Stites, N. (Ray W. Herrick Labs., Purdue Univ., West Lafayette, IN, USA); White, J.; Adams, D.E.; Triplett, M. Source: Shock and Vibration, v 16, n 2, p 117-42, 2009
14. “Continuous monitoring of the Øresund Bridge: system and data analysis” Peeters, B. (LMS Int., Leuven, Belgium); Couvreur, G.; Razinkov, O.; Kundig, C.; Van der Auweraer, H.; De Roeck, G. Source: Structure and Infrastructure Engineering, v 5, n 5, p 395-405, Oct. 2009
15. “Intelligent structural state retrieval model inspired from human memory” Wei Zheng (Key Laboratory for Optoelectronic Technology and System, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China); Weimin Chen Source: Journal of Intelligent Material Systems and Structures, v 20, n 13, p 1521-1528, September 2009
16. “Improved performance data loggers for health monitoring” Borsum, William K. (DASCOR, Escondido, CA, United States); Lizon, Richard; Perry, John; Santrizos, Tom Source: 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2005, 41st AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit
17. “Power modulation based fiber-optic loop-sensor having a dual measurement range” Nguyen, N.Q. (Dept. of Mech. & Aerosp. Eng., New York Univ., Brooklyn, NY, USA); Gupta, N. Source: Journal of Applied Physics, v 106, n 3, p 033502 (5 pp.), 1 Aug. 2009
18. “Monitoring corrosion behavior using acoustic emission techniques” Wheat, H.G. (Mechanical Engineering Department, Texas Materials Institute, University of Texas at Austin, Austin, TX 78712) Source: NACE - International Corrosion Conference Series, p 072911-0729115, 2007, Corrosion 2007
19. “A low-power wireless sensing device for remote inspection of bolted joints” Mascarenas, D.L. (Department of Structural Engineering, University of California, San Diego, CA, United States); Park, G.; Farinholt, K.M.; Todd, M.D.; Farrar, C.R. Source: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, v 223, n 5, p 565-575, August 1, 2009
20. “Physical-parameter identification of base-isolated buildings using backbone curves” Huang, Ming-Chih (Air Force Institute of Technology, Dept. of Aircraft Engineering, Gangshan 820, Taiwan); Wang, Yen-Po; Chang, Jer-Rong; Chen, Yi-Hsuan Source: Journal of Structural Engineering, v 135, n 9, p 1107-1114, 2009
21. “Health diagnosis for aircraft based on EMD and neural network” Jianguo Cui (Autom. Coll., Shenyang Inst. of Aeronaut. Eng., Shenyang, China); Xinqi Zheng; Ming Li; Zhonghai Li; Dong Liu Source: 2009 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), p 677-80, 2009
22. “Sensor requirements to monitor the real time performance of a gas turbine engine undergoing compressor blade erosion” Scala, Sinclaire M. (Concurrent Technologies Corporation, Largo, FL); Konrad, Martin; Mason, -Robert B.; Semick, Joshua; Skelton, Don Source: 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, 2004, 40th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit
23. “A hierarchical classification scheme for computationally efficient damage classification” Coelho, C.K. (Department OfMechanical and Aerospace, Engineering, Arizona State University, P.O. Box 876106, Tempe, AZ 85287-6106, United States); Das, S.; Chattopadhyay, A. Source: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, v 223, n 5, p 497-505, August 1, 2009
24. “AE monitoring for damage assessment of RC exterior beam-column subassemblages subjected to cyclic loading” Benavent-Climent, A. (Dept. of Mech. of Struct., Univ. of Granada, Granada, Spain); Castro, E.; Gallego, A. Source: Structural Health Monitoring, v 8, n 2, p 175-89, March 2009
25. “Structural damage detection using time domain periodogram analysis” Zimin, V.D. (Dept. of Mech. Eng., Univ. of Houston, Houston, TX, USA); Zimmerman, D.C. Source: Structural Health Monitoring, v 8, n 2, p 125-35, March 2009

2009-09-13T21:45:00.001-07:00

VerPro's Structural Health Monitoring Group located in Bishnupur, WB has openings for Software Engineers responsible for legacy and new product software development and developing the hardware platforms used by the software. Design responsibilities include: perform requirement analysis, software architecture design and optimization, data structure design, software partitioning for software criticality management, interface circuit requirement definition, high and low level software requirement development, control algorithm development, serial communication protocol development, programming in C#, hardware/software integration, memory usage analysis, stack usage analysis, timing analysis, throughput analysis, and other.
VerPro's Structural Health Monitoring Group is hiring Software Engineers to join the Structural Health Monitoring Team. This is an excellent opportunity to join a team to work on exciting Structural Engineering development programs. There are numerous opportunities for learning, developing, and career advancement. These positions are being created in Bishnupur, WB. Bishnupur offers many benefits. As a growing community and the 16th-centuries historical city in the India, Bishnupur has a bustling downtown area, low cost of living, award-winning schools, and diverse cultural options.

References

2009-09-06T20:59:00.001-07:00

1. “Application of real-time adaptive identification technique on damage detection and structural health monitoring” Shih-Yu Chu (Dept. of Civil Eng., Nat. Cheng Kung Univ., Tainan, Taiwan); Shih-Chieh Lo Source: Structural Control and Health Monitoring, v 16, n 2, p 154-77, March 2009

2. “Statistical analyses on multi-scale features of monitoring data from health monitoring system in long cable supported bridges” Li, Z.X. (School of Civil Engineering, Southeast University, Nanjing 210096, China); Wu, B.J.; Wang, Y. Source: Procedia Engineering, v 1, n 1, p 123-127, 2009

3. “Stiffness identification and damage localization via differential evolution algorithms” Casciati, S. (ASTRA Dept., Univ. of Catania, Siracusa, Italy) Source: Structural Control and Health Monitoring, v 15, n 3, p 436-49, April 2008

4. “Crack detection in structural systems using electro-mechanical signatures” Yuan, Lili (Faculty of Architectural Civil Engineering and Environment, Ningbo University, Ningbo 315211, China); Yan, Wei Source: 2009 Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009 - Proceedings, 2009, 2009 Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009 - Proceedings

5. “A H-QoS-demand personalized home physiological monitoring system over a wireless multi-hop relay network for mobile home healthcare applications” Lai, Chien-Chih (Department of Electronic Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan); Lee, Ren-Guey; Hsiao, Chun-Chieh; Liu, Hsin-Sheng; Chen, Chun-Chang Source: Journal of Network and Computer Applications, v 32, n 6, p 1229-1241, November 2009

6. “Computational adaptive structure assessment of c-130 flaperon” Minnetyan, Levon (Clarkson University, Potsdam, NY 13699-5710, United States); Abdi, Frank; Chamis, Christos C.; Huang, Dade Source: Procedia Engineering, v 1, n 1, p 47-50, 2009

7. “Airborne monitoring to distinguish engineered nanomaterials from incidental particles for environmental health and safety” Peters, T.M. (Dept. of Occupational & Environ. Health, Univ. of Iowa, Iowa City, IA, USA); Elzey, S.; Johnson, R.; Heaweon Park; Grassian, V.H.; Maher, T.; O'Shaughnessy, P. Source: Journal of Occupational and Environmental Hygiene, v 6, n 2, p 73-81, Feb. 2009

8. “An uncoupled approach for the design of rockfall protection tunnels” Calvetti, Francesco (Dept. of Structural Enginering, Politecnico di Milano, Italy); Di Prisco, Claudio Source: Structural Engineering International: Journal of the International Association for Bridge and Structural Engineering (IABSE), v 19, n 3, p 342-347, August 2009

9. “Physical system identification of an isolated bridge using seismic response data” Ming-Chih Huang (Dept. of Aircraft Eng., Air Force Inst. of Technol., Gangshan, Taiwan); Yen-Po Wang; Jer-Rong Chang; Chia-Shang Chang Chien Source: Structural Control and Health Monitoring, v 16, n 2, p 241-65, March 2009

10. “Fiber-optic Bragg gratings as magnetic field-insensitive strain sensors for the surveillance of cryogenic devices” Latka, Ines (Institute of Photonic Technology (IPHT), Albert-Einstein-Str. 9, D-07745 Jena, Germany); Ecke, Wolfgang; Höfer, Bernd; Habisreuther, Tobias; Willsch, Reinhardt Source: Cryogenics, v 49, n 9, p 490-496, September 2009

11. “Low stiffness variation in structural systems: identification and localization” Barone, G. (Dipt. di Ing. Strutturale e Geotecnica, Univ. degli Studi di Palermo, Palermo, Italy); Marino, F.; Pirrotta, A. Source: Structural Control and Health Monitoring, v 15, n 3, p 450-70, April 2008

12.” Identification of dynamic models of a building structure using multiple earthquake records” Ah Lum Hong (Dept. of Civil Eng. & Eng. Mech., Columbia Univ., New York, NY, USA); Betti, R.; Ching-Chang Lin Source: Structural Control and Health Monitoring, v 16, n 2, p 178-99, March 2009

13. “New HSE guidance and enforcement [occupational health and safety]” Bishop, G. (Dantherm Filtration Ltd., Leeds, UK) Source: Process Engineering, v 90, n 2, p 29-30, March-April 2009

14. “Identification of the nonlinear behaviour of a cracked RC beam through the statistical analysis of the dynamic response” Breccolotti, M. (Dept. of Civil & Environ. Eng., Univ. of Perugia, Perugia, Italy); Materazzi, A.L.; Venanzi, I. Source: Structural Control and Health Monitoring, v 15, n 3, p 416-35, April 2008

15. “Movable guyed masts affected by wind loads: buckling and stochastic response” Gioffre, M. (Dept. of Civil & Environ. Eng., Univ. of Perugia, Perugia, Italy); Cavalagli, N.; Gusella, V. Source: Structural Control and Health Monitoring, v 15, n 3, p 299-314, April 2008

16. “Loading models and response control of footbridges excited by running pedestrians” Occhiuzzi, A. (Dept. of Technol., Univ. of Napoli 'Parthenope', Naples, Italy); Spizzuoco, M.; Ricciardelli, F. Source: Structural Control and Health Monitoring, v 15, n 3, p 349-68, April 2008

17. “Condition assessment and dynamic system identification of a historic suspension footbridge” Gentile, C. (Dept. of Struct. Eng., Polytech. of Milan, Milan, Italy); Gallino, N. Source: Structural Control and Health Monitoring, v 15, n 3, p 369-88, April 2008

18. “FEM-based reinforcement design of surge shaft: Illustrated with a case study of Xiaowan hydropower plant” Zhang, Yuting (State Key Laboratory of Water Resources, Hydropower Engineering Science Wuhan University, Wuhan 430072, China); Xiao, Ming; Chen, Juntao Source: 2009 Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009 - Proceedings, 2009, 2009 Asia-Pacific Power and Energy Engineering Conference, APPEEC 2009 - Proceedings

19. “Input force identification: application to soil-pile interaction” Ai-Lun Wu (Dept. of Civil Eng., Nat. Taiwan Univ., Taipei, Taiwan); Chin-Hsiung Loh; Yang, J.N.; Jian-Huang Weng; Chia-Han Chen; Tzou-Shin Ueng Source: Structural Control and Health Monitoring, v 16, n 2, p 223-40, March 2009

20. “Applying surrogates and indicators to assess removal efficiency of trace organic chemicals during chemical oxidation of wastewaters” Dickenson, Eric R. V. (Advanced Water Technology Center (AQWATEC), Environmental Science and Engineering Division, Colorado School of Mines, Golden, CO 80401, United States); Drewes, Jörg E.; Sedlak, David L.; Wert, Eric C.; Snyder, Shane A. Source: Environmental Science and Technology, v 43, n 16, p 6242-6247, August 15, 2009

21. “Hydroelastic natural vibrations of perforated plates with cracks” Liu, Tong (Thermal Energy Enging. Res. Center, Zhengzhou University, 97 Wenhua Rd, 450002, China); Wang, Ke; Dong, Qi-Wu; Liu, Min-Shan Source: Procedia Engineering, v 1, n 1, p 129-133, 2009

22. “Experimental and numerical analysis of vibrations induced by underground trains in an urban environment” Aiello, V. (Dept. of Soil Defense, Univ. of Calabria, Rende, Italy); Boiero, D.; D'Apuzzo, M.; Socco, L.V.; Silvestri, F. Source: Structural Control and Health Monitoring, v 15, n 3, p 315-48, April 2008

23. “A form to follow function: recladding a regional healthcare facility” Dean, S.W. (Curtain Wall Design & Consulting, Inc. (CDC, Inc.), Canton, CT, USA) Source: Journal of ASTM International, v 5, n 7, p JAI100865 (12 pp.), 1 July 2008

24. “Application of hybrid genetic algorithm to system identification” Wang, G.S. (Dept. of Constr. Eng., Chaoyang Univ. of Technol., Taichung, Taiwan) Source: Structural Control and Health Monitoring, v 16, n 2, p 125-53, March 2009

review of SHM

2009-08-17T08:34:00.001-07:00

Liew and Veidt [1] proposed guided waves based damage identification technique with neural networks through a data fusion process that associates overlapping intermediate test results while isolating outliers to narrow the training range for improved generalization in the iterative test inputs dependent training scheme.
Rosalie et al [2] implemented cross-borehole tomography using a simultaneous iterative reconstruction technique to detect damages in flat plates; where Images were reconstructed from the generation of simulated data for sample areas containing damage zones of different shapes and sizes located at various positions within the region.
Lallart et al [3] introduced the architecture of a fully integrated, self-powered structural health monitoring scheme using a micro-generators to power an array of numerous distributed actuators and sensors as well as wireless data transmission modules without recurring to heavy and costly wiring.
Singh [4] used transcendental inverse eigenvalue problems for estimation of the damage parameters of a rod by using only few and selected eigenvalues corresponding to the measured resonant and anti-resonant frequencies.
Guo et al [5] presented an equivalent vehicle load method to find the relationship between fatigue damage, temperature and increasing traffic flow for fatigue life assessment of critical bridge members using statistical disposition of traffic flow and finite element analysis.
Hison et al [6] reported a magneto-elastic sensor prototype for on-line elastic deformation monitoring and fracture alarm in civil engineering structure.
Ling et al [7] used embedded fiber-optic Bragg grating sensors to measure the dynamic strain and identify the existence of delamination of the composite structures to allow the continuous estimation of fatigue life and minimize the need of in-site inspection of the structures.
Tsuda et al [8] showed that fiber Bragg grating sensors could determine the location of fatigue crack tip more precisely than piezoelectric sensors.
Zimmerman [9] investigated the effect of measurement noise on damage detection performance using closed-form solutions for the sensitivity of the damage vectors of minimum rank perturbation theory.
Sarazin and Newhook [10] discussed the development of a strain-based index in which the loss of strain compatibility can be used to identify regions where debonding of carbon fiber reinforced polymer strengthen concrete has occurred.
Tang et al [11] proposed an online sequential weighted least squares support vector machine technique to identify the structural parameters and their changes when vibration data involve damage events.
Sekine and Atobe [12] identified of locations and force histories of multiple point impacts on composite isogrid-stiffened panels using measured longitudinal strain responses of isogrid.
Yoshitomi and Takewaki [13] proposed a noise-bias compensation method to evaluate the intensity of noise in addition to the identification of story stiffness and damping performing numerical simulations in the frequency domain by generating two stationary random processes with the specified levels of power spectra.
Wang et al [14] studied long term health monitoring of fiber-optic sensors embedded glass fiber-reinforced polymer bars for near-surface mounted reinforcement for the strengthening of reinforced concrete structures.
1. Pattern recognition of guided waves for damage evaluation in bars Pattern Recognition Letters, Volume 30, Issue 3, 1 February 2009, Pages 321-330 Chin Kian Liew, Martin Veidt
2. Structural health monitoring of composite structures using stress wave methods Composite Structures, Volume 67, Issue 2, February 2005, Pages 157-166 C. Rosalie, A. Chan, W.K. Chiu, S.C. Galea, F. Rose, N. Rajic
3. Synchronized switch harvesting applied to self-powered smart systems: Piezoactive microgenerators for autonomous wireless receivers Sensors and Actuators A: Physical, Volume 147, Issue 1, 15 September 2008, Pages 263-272 Mickaël Lallart, Daniel Guyomar, Yves Jayet, Lionel Petit, Elie Lefeuvre, Thomas Monnier, Philippe Guy, Claude Richard
4. Transcendental inverse eigenvalue problems in damage parameter estimation Mechanical Systems and Signal Processing, Volume 23, Issue 6, August 2009, Pages 1870-1883 Kumar Vikram Singh
5. Influence of ambient temperature on the fatigue damage of welded bridge decks International Journal of Fatigue, Volume 30, Issue 6, June 2008, Pages 1092-1102 Tong Guo, Aiqun Li, Hao Wang
6. Magnetoelastic sensor for real-time monitoring of elastic deformation and fracture alarm Sensors and Actuators A: Physical, Volume 125, Issue 1, 21 October 2005, Pages 10-14 Cornelia Hison, Giovanni Ausanio, Alberto C. Barone, Vincenzo Iannotti, Eufemia Pepe, Luciano Lanotte
7. Determination of dynamic strain profile and delamination detection of composite structures using embedded multiplexed fibre-optic sensors Composite Structures, Volume 66, Issues 1-4, October-December 2004, Pages 317-326 Hang-Yin Ling, Kin-Tak Lau, Li Cheng
8. Investigation of fatigue crack in stainless steel using a mobile fiber Bragg grating ultrasonic sensor Optical Fiber Technology, Volume 13, Issue 3, July 2007, Pages 209-214 Hiroshi Tsuda, Jung-Ryul Lee, Yisheng Guan, Junji Takatsubo
9. Statistical confidence using minimum rank perturbation theory Mechanical Systems and Signal Processing, Volume 20, Issue 5, July 2006, Pages 1155-1172 David C. Zimmerman
10. A strain-based index for monitoring laminates of FRP-strengthened beams Construction and Building Materials, Volume 21, Issue 4, April 2007, Pages 789-798 Geoff A. Sarazin, John P. Newhook
11. Online weighted LS-SVM for hysteretic structural system identification Engineering Structures, Volume 28, Issue 12, October 2006, Pages 1728-1735 He-Sheng Tang, Song-Tao Xue, Rong Chen, Tadanobu Sato
12. Identification of locations and force histories of multiple point impacts on composite isogrid-stiffened panels Composite Structures, Volume 89, Issue 1, June 2009, Pages 1-7 Hideki Sekine, Satoshi Atobe
13. Noise-bias compensation in physical-parameter system identification under microtremor input Engineering Structures, Volume 31, Issue 2, February 2009, Pages 580-590 S. Yoshitomi, I. Takewaki
14. Strain monitoring of RC members strengthened with smart NSM FRP bars Construction and Building Materials, Volume 23, Issue 4, April 2009, Pages 1698-1711 Bo Wang, J.G. Teng, Laura De Lorenzis, Li-Min Zhou, Jinping Ou, Wei Jin, K.T. Lau

SHM review

2009-08-13T08:05:00.000-07:00

Wu and Li [1] developed a two-stage eigensensitivity-based finite element model updating procedure for structural parameter identification and damage detection on the basis of ambient vibration measurements.
Assuming change of crack closure as a function of load, Cobb et al [2] indicated that cracking in open holes can be detected in the fatiguing process using the energy ratio measured by the ratio computed for the undamaged hole.
For monitoring the integrity of plate-like structures over large areas, Michaels and Michaels [3] used digital band-pass filter of broadband Lamb waves generated from an impulsive excitation in a permanently attached spatially distributed array of piezoelectric transducers.
Guyomar et al [4] discussed the design and integration of micro-generators to power a wireless, self-powered, transmitter for health monitoring networks, which directly convert mechanical energy into electrical energy optimally, using synchronized switch harvesting method based on the nonlinear processing of the piezoelectric voltage.
Mustapha et al [5] experimentally identified structural defects based on novelty detection, outlier analysis, and multi-layer perceptron using scattering wave generated from piezoelectric patches bonded on the plate, forming a network.
Whelan et al [6] deployed a large-scale wireless sensor network for ambient vibration testing of a single-span integral abutment bridge to derive in-service modal parameters; where natural frequencies, damping ratios, and mode shapes are calculated from the measurement of accelerometers with a real-time data collection from a 40-channel single network operating and a sampling rate of 128 Hz per sensor.
Ryu et al [7] considered fiber Bragg grating strain sensing system using a wavelength-swept fiber laser for the realization of a smart composite wing box.
Fujimoto and Sekine [8] presented a method for identification of the locations and shapes of crack and disbond fronts in aircraft structural panels repaired with bonded composite patches by minimizing the residual norm between the measured in-plane strain range on a strain measurement plane in the composite patches and the calculated in-plane strain range.
Li et al [9] described a methodology for the health monitoring of composite marine joint structures based on strain measurements under operational loading using embedded fiber Bragg grating sensors.
Butcher et al [10] suggested using of electrical conductivity to separate defects accumulated in structural members from those in joints.
Sundaresan et al [11] discussed the use of integrated and distributed sensors to develop intelligent large composite structures for monitoring with minimal human intervention.
Mujica et al [12] presented principal component analysis, partial least square, multi-way principal component analysis and multi-way partial least square for reducing dimensionality in damage identification problem, in particular, detecting and locating impacts in a part of a commercial aircraft wing flap.
Xia et al [13] investigated the variation of frequencies, mode shapes and damping with respect to temperature and humidity changes of a reinforced concrete slab; and shown that the frequencies have a strong negative correlation with temperature and humidity, damping ratios have a positive correlation, but no clear correlation of mode shapes with temperature and humidity change; and concluded from a linear regression model that the variation of the elastic modulus of the material could be the only criteria to be considered.
Capoluongo et al [14] exploited fiber Bragg grating sensors with an adequate interrogation system to reveal the presence of damage on a structure,
Since greater information content is localized at higher frequencies, modal analysis tests in a wide frequency range were performed by Capoluongo et al [14] in order to verify the performances of fiber Bragg grating sensors with an adequate interrogation system to retrieve high frequency structural dynamic features for structural health monitoring.
1. Structural parameter identification and damage detection for a steel structure using a two-stage finite element model updating method Journal of Constructional Steel Research, Volume 62, Issue 3, March 2006, Pages 231-239 J.R. Wu, Q.S. Li
2. An automated time–frequency approach for ultrasonic monitoring of fastener hole cracks NDT & E International, Volume 40, Issue 7, October 2007, Pages 525-536 Adam C. Cobb, Jennifer E. Michaels, Thomas E. Michaels
3. Guided wave signal processing and image fusion for in situ damage localization in plates Wave Motion, Volume 44, Issue 6, June 2007, Pages 482-492 Jennifer E. Michaels, Thomas E. Michaels
4. Synchronized switch harvesting applied to selfpowered smart systems: Piezoactive microgenerators for autonomous wireless transmitters Sensors and Actuators A: Physical, Volume 138, Issue 1, 20 July 2007, Pages 151-160 Daniel Guyomar, Yves Jayet, Lionel Petit, Elie Lefeuvre, Thomas Monnier, Claude Richard, Mickaël Lallart
5. Damage location in an isotropic plate using a vector of novelty indices Mechanical Systems and Signal Processing, Volume 21, Issue 4, May 2007, Pages 1885-1906 F. Mustapha, G. Manson, K. Worden, S.G. Pierce
6. Real-time wireless vibration monitoring for operational modal analysis of an integral abutment highway bridge Engineering Structures, In Press, Corrected Proof, Available online 7 May 2009 Matthew J. Whelan, Michael V. Gangone, Kerop D. Janoyan, Ratneshwar Jha
7. Buckling behavior monitoring of a composite wing box using multiplexed and multi-channeled built-in fiber Bragg grating strain sensors NDT & E International, Volume 41, Issue 7, October 2008, Pages 534-543 Chi-Young Ryu, Jung-Ryul Lee, Chun-Gon Kim, Chang-Sun Hong
8. Identification of crack and disbond fronts in repaired aircraft structural panels with bonded FRP composite patches Composite Structures, Volume 77, Issue 4, February 2007, Pages 533-545 Shin-etsu Fujimoto, Hideki Sekine
9. Health monitoring of marine composite structural joints using fibre optic sensors Composite Structures, Volume 75, Issues 1-4, September 2006, Pages 321-327 H.C.H. Li, I. Herszberg, C.E. Davis, A.P. Mouritz, S.C. Galea
10. On the separation of internal and boundary damage from combined measurements of electrical conductivity and vibration frequencies International Journal of Engineering Science, Volume 46, Issue 10, October 2008, Pages 968-975 Eric A. Butcher, Igor Sevostianov, Thomas Burton
11. Methods of distributed sensing for health monitoring of composite material structures Composites Part A: Applied Science and Manufacturing, Volume 32, Issue 9, September 2001, Pages 1357-1374 M. J. Sundaresan, P. F. Pai, A. Ghoshal, M. J. Schulz, F. Ferguson, J. H. Chung
12. Multivariate statistics process control for dimensionality reduction in structural assessment Mechanical Systems and Signal Processing, Volume 22, Issue 1, January 2008, Pages 155-171 L.E. Mujica, J. Vehí, M. Ruiz, M. Verleysen, W. Staszewski, K. Worden
13. Long term vibration monitoring of an RC slab: Temperature and humidity effect Engineering Structures, Volume 28, Issue 3, February 2006, Pages 441-452 Yong Xia, Hong Hao, Giovanna Zanardo, Andrew Deeks
14. Modal analysis and damage detection by Fiber Bragg grating sensors Sensors and Actuators A: Physical, Volume 133, Issue 2, 12 February 2007, Pages 415-424 P. Capoluongo, C. Ambrosino, S. Campopiano, A. Cutolo, M. Giordano, I. Bovio, L. Lecce, A. Cusano

2009-08-12T08:04:00.000-07:00

Silva and Roberto [1] presented a strain-based methodology for the health monitoring of composite joints based on strain measurements using distributed embedded fiber Bragg grating sensors to detect crack propagation and then validated with finite element model to correlate experimental strain measurements prior to cyclic loading with the numerical predictions and to determine the sensitivity of the sensors to changes in longitudinal strain due to crack growth.
Yuan et al [2] developed wide-band Lamb wave based on-line delamination and impact damage detection technique of honeycomb sandwich and carbon fiber composite structures through a trained Kohonen neural network, while eliminating the influence of different distances between the actuator and sensor.
Vishnuvardhan et al [3] conducted experiment on graphite-epoxy composite plate using printed circuit board-based single-transmitter multiple-receiver arrays for material characterization and structural health monitoring of anisotropic plate-like structures; where the reconstruction of the material state was carried out by utilizing a phased addition reconstruction algorithm.
Bhalla et al [4] addressed major technological issues and challenges associated with structural monitoring of underground structures.
Wang and Ong [5] presented structural damage detection scheme using autoregressive-model incorporating multivariate exponentially weighted moving average control chart; which comprises procedures based on the undamaged or reference state of the structure being monitored and those based on its damaged or current state.
Efstathiades et al [6] studied health monitoring problem using artificial neural network in order to identify possible imperfections in a typical curtain-wall system.
Qiu and Yuan [7] developed an integrated multi-channel piezoelectric array scanning system for the purpose of structural health monitoring with a gain programmable charge amplifier and a low crosstalk scanning module; where hardware was managed using a LabVIEW platform based integrated software.
Rutherford et al [8] presented a non-linear feature identification technique in the form of autoregressive coefficients in the frequency domain autoregressive model with exogenous inputs for structural damage detection using the impedance-based structural health monitoring method, which utilizes electromechanical coupling properties of piezoelectric materials.
Qing et al [9] investigated experimentally the effect of adhesive thickness and its modulus on the performance of adhesively bonded piezoelectric elements for the purpose of structural health monitoring.
Kousourakis et al [10] experimentally investigated the effect of comparative vacuum monitoring galleries on the mode I delamination toughness, interlaminar shear strength, and impact damage resistance of a carbon/epoxy laminate.
Yuen and Lam [11] presented a Bayesian probabilistic method to select the artificial neural network architecture for structural health monitoring.
Chiu et al [12] presented a set of numerical results on the use of Lamb waves for the monitoring of crack growth in the lower wing skin aircraft structures adhesively bonded with a composite repair patch.
Moyo et al [13] reported some results from a multi-disciplinary research program on fiber Bragg grating sensors.
Wang and Qiao [14] used the peak value appearing on the irregularity profile of a beam extracted from the mode shape by a numerical filter to determine the location and size of the crack.
1. Structural health monitoring of marine composite structural joints using embedded fiber Bragg grating strain sensors Composite Structures, Volume 89, Issue 2, June 2009, Pages 224-234 Rodrigo A. Silva-Muñoz, Roberto A. Lopez-Anido.
2. Neural network method based on a new damage signature for structural health monitoring Thin-Walled Structures, Volume 43, Issue 4, April 2005, Pages 553-563 Shenfang Yuan, Lei Wang, Ge Peng.
3. Structural health monitoring of anisotropic plates using ultrasonic guided wave STMR array patches NDT & E International, Volume 42, Issue 3, April 2009, Pages 193-198 J. Vishnuvardhan, Ajith Muralidharan, C.V. Krishnamurthy, Krishnan Balasubramaniam.
4. Structural health monitoring of underground facilities – Technological issues and challenges Tunnelling and Underground Space Technology, Volume 20, Issue 5, September 2005, Pages 487-500 S. Bhalla, Y.W. Yang, J. Zhao, C.K. Soh.
5. Structural damage detection using autoregressive-model-incorporating multivariate exponentially weighted moving average control chart Engineering Structures, Volume 31, Issue 5, May 2009, Pages 1265-1275 Zengrong Wang, K.C.G. Ong.
6. Application of neural networks for the structural health monitoring in curtain-wall systems Engineering Structures, Volume 29, Issue 12, December 2007, Pages 3475-3484 Ch. Efstathiades, C.C. Baniotopoulos, P. Nazarko, L. Ziemianski, G.E. Stavroulakis.
7. On development of a multi-channel PZT array scanning system and its evaluating application on UAV wing box Sensors and Actuators A: Physical, Volume 151, Issue 2, 29 April 2009, Pages 220-230 Lie Qiu, Shenfang Yuan.
8. Non-linear feature identifications based on self-sensing impedance measurements for structural health assessment Mechanical Systems and Signal Processing, Volume 21, Issue 1, January 2007, Pages 322-333 Amanda C. Rutherford, Gyuhae Park, Charles R. Farrar.
9. Effect of adhesive on the performance of piezoelectric elements used to monitor structural health International Journal of Adhesion and Adhesives, Volume 26, Issue 8, December 2006, Pages 622-628 Xinlin P. Qing, Hian-Leng Chan, Shawn J. Beard, Teng K. Ooi, Stephen A. Marotta.
10. Interlaminar properties of polymer laminates containing internal sensor cavities Composite Structures, Volume 75, Issues 1-4, September 2006, Pages 610-618 A. Kousourakis, A.P. Mouritz, M.K. Bannister.
11. On the complexity of artificial neural networks for smart structures monitoring Engineering Structures, Volume 28, Issue 7, June 2006, Pages 977-984 Ka-Veng Yuen, Heung-Fai Lam.
12. The effects of structural variations on the health monitoring of composite structures Composite Structures, Volume 87, Issue 2, January 2009, Pages 121-140 W.K. Chiu, T. Tian, F.K. Chang.
13. Development of fiber Bragg grating sensors for monitoring civil infrastructure Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1828-1834 P. Moyo, J.M.W. Brownjohn, R. Suresh, S.C. Tjin.
14. On irregularity-based damage detection method for cracked beams International Journal of Solids and Structures, Volume 45, Issue 2, 15 January 2008, Pages 688-704 Jialai Wang, Pizhong Qiao.

review of SHM

2009-08-10T07:57:00.000-07:00

Saadat [1] et al applied an intelligent parameter varying modeling and system identification technique to detect damage in base-excited structures and studied the structural non-linearities and ground excitation characteristics with wavelet analysis.
A structural health monitoring method was explored by Todorovska and Trifunac [2] using strong motion data from the Imperial Valley Earthquake of 1979 recorded in the former Imperial County Services Building, severely damaged by this earthquake based on changes in wave travel time; which was measured from impulse response functions computed from the recorded horizontal seismic response in three time windows—before, during, and after the largest amplitude response, as determined from previous studies of this building, based on analysis of novelties in the recorded response.
Yan et al [3] applied a structural health monitoring technique through a two-step procedure, namely a clustering of the data space into several regions and then the application of Principle Component Analysis in each local region of a real bridge using vibration data measured in situ over a one-year period.
Whittingham et al [4] presented a structural health monitoring technique based on analysis of the dynamic signature of the thick GFRP laminates, which had various degrees of damage in the form of delaminations and showed that it is possible to quantify the effect of damage on the acoustic response in plate specimens and T-joint specimens.
Lestari and Qiao [5] conducted a structural dynamics response based experimental health monitoring of FRP honeycomb sandwich structures using piezoelectric sensors; where artificial damage was created between the interface of core and faceplate and also in the core of sandwich beam to simulate core–faceplate debonding and core crushing, respectively.
Wang and Ong [6] presented a structural health monitoring scheme based on time series analysis and multivariate statistical process control techniques.
Katsikeros and Labeas [7] demonstrated structural health monitoring methodology and the identification of its capabilities in the prediction of fatigue damage states of a typical aircraft cracked lap-joint structure using Artificial Neural Network.
Kirikera et al [8] developed passive structural neural system, which uses electronic logic circuits to mimic the signal processing in the biological neural system to detect ambient Lamb waves or bulk waves that are produced by cracking, delamination, bearing damage, rotor imbalance, flow instabilities, impacts, or other material failure modes
Carden and Brownjohn [9] used autoregressive moving average based classifier for unsupervised learning when the structural response exhibits change.
Dai et al [10] proposed a fiber Bragg grating sensor system for measurement of strain and temperature based on time-division multiplexing suitable for the application in structural health monitoring, where large numbers of sensors are required in wide measurement ranges.
Tessler and Spangler [11] formulated a variational principle for the inverse problem of full-field reconstruction of three-dimensional plate/shell deformations from experimentally measured surface strains which was based upon the minimization of least-squares functional that used the complete set of strain measures consistent with linear, first-order shear-deformation theory.
For damage detection using output-only vibration measurements under changing environmental conditions, Deraemaeker et al [12] identified eigen-properties of the structure using an automated stochastic subspace identification procedure and peak indicators from the Fourier transform of modal filters.
Park et al [13] proposed an outlier analysis based on Mahalanobis squared distance by taking root mean square deviation values of impedance signatures as a damage-sensitive feature vector for detection of different types of the railroad track damage, including head damage, web damage, and flange damage using a macro-fiber composite impedance-based wireless structural health monitoring system.
Catbas et al [14] presented the reliability estimation studies for the main truss components as well as the entire structural system of a long span truss bridge to assess the safety level by using a probabilistic approach in terms of its component and system reliability indices.

1. Structural health monitoring and damage detection using an intelligent parameter varying (IPV) technique International Journal of Non-Linear Mechanics, Volume 39, Issue 10, December 2004, Pages 1687-1697 Soheil Saadat, Mohammad N. Noori, Gregory D. Buckner, Tadatoshi Furukawa, Yoshiyuki Suzuki
2. Earthquake damage detection in the Imperial County Services Building III: Analysis of wave travel times via impulse response functions Soil Dynamics and Earthquake Engineering, Volume 28, Issue 5, May 2008, Pages 387-404 Maria I. Todorovska, Mihailo D. Trifunac
3. Structural damage diagnosis under varying environmental conditions—part II: local PCA for non-linear cases Mechanical Systems and Signal Processing, Volume 19, Issue 4, July 2005, Pages 865-880 A.-M. Yan, G. Kerschen, P. De Boe, J.-C. Golinval
4. Disbond detection in adhesively bonded composite structures using vibration signatures Composite Structures, Volume 75, Issues 1-4, September 2006, Pages 351-363 B. Whittingham, H.C.H. Li, I. Herszberg, W.K. Chiu
5. Damage detection of fiber-reinforced polymer honeycomb sandwich beams Composite Structures, Volume 67, Issue 3, March 2005, Pages 365-373 Wahyu Lestari, Pizhong Qiao
6. Autoregressive coefficients based Hotelling’s T2 control chart for structural health monitoring Computers & Structures, Volume 86, Issues 19-20, October 2008, Pages 1918-1935 Zengrong Wang, K.C.G. Ong
7. Development and validation of a strain-based Structural Health Monitoring system Mechanical Systems and Signal Processing, Volume 23, Issue 2, February 2009, Pages 372-383 Ch.E. Katsikeros, G.N. Labeas
8. Damage localisation in composite and metallic structures using a structural neural system and simulated acoustic emissions Mechanical Systems and Signal Processing, Volume 21, Issue 1, January 2007, Pages 280-297 Goutham R. Kirikera, Vishal Shinde, Mark J. Schulz, Anindya Ghoshal, Mannur Sundaresan, Randall Allemang
9. ARMA modelled time-series classification for structural health monitoring of civil infrastructure Mechanical Systems and Signal Processing, Volume 22, Issue 2, February 2008, Pages 295-314 E. Peter Carden, James M.W. Brownjohn
10. A novel time-division multiplexing fiber Bragg grating sensor interrogator for structural health monitoring Optics and Lasers in Engineering, In Press, Corrected Proof, Available online 26 June 2009 Yongbo Dai, Yanju Liu, Jinsong Leng, Gang Deng, Anand Asundi
11. A least-squares variational method for full-field reconstruction of elastic deformations in shear-deformable plates and shells Computer Methods in Applied Mechanics and Engineering, Volume 194, Issues 2-5, 4 February 2005, Pages 327-339 Alexander Tessler, Jan L. Spangler
12. Vibration-based structural health monitoring using output-only measurements under changing environment Mechanical Systems and Signal Processing, Volume 22, Issue 1, January 2008, Pages 34-56 A. Deraemaeker, E. Reynders, G. De Roeck, J. Kullaa
13. An outlier analysis of MFC-based impedance sensing data for wireless structural health monitoring of railroad tracks Engineering Structures, Volume 30, Issue 10, October 2008, Pages 2792-2799 Seunghee Park, Daniel J. Inman, Chung-Bang Yun
14. Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data Engineering Structures, Volume 30, Issue 9, September 2008, Pages 2347-2359 F. Necati Catbas, Melih Susoy, Dan M. Frangopol

Review

2009-08-07T08:57:00.000-07:00

Nichols [1] explored the role of ambient excitation and empirical modeling in detecting damage in an offshore structure excited with the output of a stochastic process conforming to the ambient Pierson–Moskowitz wave distribution.
Ghoshal et al [2] explored four different algorithms, like transmittance function, resonant comparison, operational deflection shape, and wave propagation methods for detecting damage by moisture absorption, fatigue, wind gusts or lightening strikes on wind turbine blades through the measurement of the vibration response when it is excited using piezoceramic actuator bonded to the blade and sensed through either piezoceramic sensor patches bonded to the blade, or a scanning laser doppler vibrometer.
Liberatore et al [3] developed a model based fault detection filter which is based on a left eigenstructure assignment approach to accommodate system sensitivities that are revealed as ill-conditioned matrices formed from the eigenvectors in the construction of the detection filter gains.
The impact damages were detected by Takeda et al [4] using the spectrum change of the wavelength shift of the FBG sensor output for the durability tests of a composite wing structure and then validated by using NDI (non-destructive inspection) technologies: AE (acoustic emission) sensors, an ultrasonic C-scan, and pulsed heating thermography.
Ko and Ni [5] explored long term structural health monitoring for large-scale bridge, in order to secure structural and operational safety and issue early warnings on damage or deterioration prior to costly repair or even catastrophic collapse.
Fritzen and Kraemer [6] discussed the use of modal information as well as the direct use of forced and ambient vibrations in the time and frequency domain for SHM of civil and aerospace engineering structures as well as off-shore wind energy plants, where sensor networks, actuators and computational capabilities are used to enable a structure to perform a self-diagnosis with the goal that this structure can release early warnings about a critical health state, locate and classify damage or even to forecast the remaining life-time.
Taha and Lucero [7] introduced pattern recognition and damage detection methods with fuzzy sets; where Bayesian updating was used to demarcate levels of damage into fuzzy sets accommodating the uncertainty associated with the ambiguous damage states.
To evaluate structural condition, Catbas et al [8] used multi-input–multi-output dynamic data to obtain modal flexibility and then the curvature is calculated from the deflected shapes using this modal flexibility as opposed to using modal vectors.
Kawchuk et al [9] employed structural health monitoring techniques to identify the presence, location and magnitude of structural alterations within the spine using frequency response functions and artificial neural network.

Silva et al [10] investigated structural health monitoring using vibration data in a three-stage process: reduction of the time-series data using principle component analysis, the development of a data-based model using an auto-regressive moving average model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach, like fuzzy c-means, Gustafson–Kessel algorithms.
Achenbach [11] have discussed about the health monitoring of safety-critical structures such as aircraft, bridges, nuclear reactors and dams, which cannot be allowed to fail; using the techniques of non-destructive inspection to provide continuous or on-demand information about the state of a structure, so that an assessment of the structural integrity can be made at any time, and timely remedial actions may be taken as necessary through a large number of sensors.
Teo et al [12] demonstrated the optimum combinations of frequency and location of the sensor in monitoring sub-surface defects using scattering of stress waves on aircraft structures with multi-layered construction and geometry variation.
Li and Chan [13] developed a structural health assessment methodology using data acquired from structural health monitoring system installed on long-span bridges considering a fatigue crack growth criterion based on the concept of the continuum damage mechanics for pre-determining the global state of the bridge.
Mayer et al [14] examined model based monitoring of structures using piezoelectric actuators.

1. Structural health monitoring of offshore structures using ambient excitation Applied Ocean Research, Volume 25, Issue 3, June 2003, Pages 101-114 J. M. Nichols
2. Structural health monitoring techniques for wind turbine blades Journal of Wind Engineering and Industrial Aerodynamics, Volume 85, Issue 3, 24 April 2000, Pages 309-324 Anindya Ghoshal, Mannur J. Sundaresan, Mark J. Schulz, P. Frank Pai
3. Application of a fault detection filter to structural health monitoring Automatica, Volume 42, Issue 7, July 2006, Pages 1199-1209 Sauro Liberatore, Jason L. Speyer, Andy Chunliang Hsu
4. Structural health monitoring of composite wing structure during durability test Composite Structures, Volume 79, Issue 1, June 2007, Pages 133-139 S. Takeda, Y. Aoki, T. Ishikawa, N. Takeda, H. Kikukawa
5. Technology developments in structural health monitoring of large-scale bridges Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1715-1725 J.M. Ko, Y.Q. Ni
6. Self-diagnosis of smart structures based on dynamical properties Mechanical Systems and Signal Processing, Volume 23, Issue 6, August 2009, Pages 1830-1845 C.-P. Fritzen, P. Kraemer
7. Damage identification for structural health monitoring using fuzzy pattern recognition Engineering Structures, Volume 27, Issue 12, October 2005, Pages 1774-1783 M.M. Reda Taha, J. Lucero
8. Conceptual damage-sensitive features for structural health monitoring: Laboratory and field demonstrations Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1650-1669 F. Necati Catbas, Mustafa Gul, Jason L. Burkett
9. Structural health monitoring to detect the presence, location and magnitude of structural damage in cadaveric porcine spinesJournal of Biomechanics, Volume 42, Issue 2, 19 January 2009, Pages 109-115 Gregory Neil Kawchuk, Colleen Decker, Ryan Dolan, Jason Carey
10. Structural damage detection by fuzzy clustering Mechanical Systems and Signal Processing, Volume 22, Issue 7, October 2008, Pages 1636-1649 Samuel da Silva, Milton Dias Júnior, Vicente Lopes Junior, Michael J. Brennan
11. Structural health monitoring – What is the prescription? Mechanics Research Communications, Volume 36, Issue 2, March 2009, Pages 137-142 Jan D. Achenbach
12. Optimal placement of sensors for sub-surface fatigue crack monitoring Theoretical and Applied Fracture Mechanics, In Press, Corrected Proof, Available online 14 July 2009 Y.H. Teo, W.K. Chiu, F.K. Chang, N. Rajic
13. Fatigue criteria for integrity assessment of long-span steel bridge with health monitoring Theoretical and Applied Fracture Mechanics, Volume 46, Issue 2, October 2006, Pages 114-127 Z.X. Li, T.H.T. Chan
14. An approach for the model based monitoring of piezoelectric actuators Computers & Structures, Volume 86, Issues 3-5, February 2008, Pages 314-321 Dirk Mayer, Heiko Atzrodt, Sven Herold, Martin Thomaier

Review

2009-08-06T08:53:00.000-07:00

Giurgiutiu [1] has reviewed various aspects of signal processing, spectra collection, data processing and analysis, pattern recognition, and decision making for signal processing and damage identification/pattern recognition algorithms in structural health monitoring.
Verijenko and Verijenko [2] proposed a passive peak monitoring, cost effective structural health monitoring technique using strain memory alloys; which are ferrous alloys that display paramagnetism in the unstrained state, but transform proportionally to display varying degrees of ferromagnetism depending on the level of peak strain induced in the material. This effect is achieved using a transformation in crystal structure from a meta-stable austenitic structure to a stable strain-induced martensitic structure.
Chase [3] et al developed adaptive recursive least squares filtering techniques for structural health monitoring using measured or estimated structural responses by identifying changes in structural parameters, like comparing the damage stiffness matrix of a structure with the undamaged model matrix.
Park and Sohn [4] developed parameter estimation techniques to automatically estimate model parameters for health monitoring of structure as a statistical pattern recognition problem; where decision boundary for outlier is based on the generalized extreme value distribution.
Majumder et al [5] reviewed structural health monitoring using FBG sensors.
Yinghui and Michaels [6] presented a methodology for applying diffuse ultrasonic waves for structural health monitoring in the presence of unmeasured temperature changes; which offer the advantages of simplicity of signal generation and reception, sensitivity to damage, and large area coverage.
A fiber optic acoustic emission sensor based structural health monitoring technique is proposed by Fu et al [7] using fused-tapered coupler.
Baker et al [8] presented a simple strain-based structural health monitoring approach for monitoring the boron/epoxy patch repair of a critical fatigue crack in an F-111C wing.
Bernini et al [9] demonstrated the viability of fiber-optic frequency-domain Brillouin strain sensing for accurate high-resolution structural health monitoring; where high performances have been achieved by applying an iterative reconstruction algorithm considering the influence of the acoustic wave involved in Brillouin scattering.
Chan et al [10] developed the FBG sensors for structural health monitoring to investigate the feasibility via monitoring the strain of different parts of the Tsing Ma Bridge as well as validated the performance with the conventional wind and structural health monitoring system.
The extrinsic Fabry–Perot interferometer and fiber Bragg grating sensors have been real-time employed by Leng and Asundi [11] to simultaneously monitoring the cure process of CFRP composite laminates with and without damage.
Kister et al [12] presented optical fiber Bragg grating sensors based structural health monitoring of West Mill Bridge which is a glass and carbon fiber composite road bridge.
Brown and Adams [13] described the structural health monitoring for evolution of a reversible damage in a bolted fastener and show that the evolution of damage is sensitive to both temporal and spatial bifurcation parameters assuming the damage indicator behaves like a stable quasi-stationary equilibrium point in a subsidiary non-linear bifurcating system within the damage center manifold.
Koh and Dyke [14] used correlation-based damage detection methods for long-span, cable-stayed bridges based on the multiple damage location assurance criterion, which combines a correlation-based technique with a forward-type estimation of damage-sensitive structural parameters; where the locations of damage are determined by iteratively searching for the combination of structural parameters that maximizes the correlation coefficient through the application of genetic algorithms.

1. Signal Processing and Pattern Recognition for Pwas-based Structural Health Monitoring Structural Health Monitoring, 2008, Pages 589-656 Victor Giurgiutiu
2. The use of strain memory alloys in structural health monitoring systems Composite Structures, Volume 76, Issues 1-2, October 2006, Pages 190-196 B. Verijenko, V. Verijenko
3. Efficient structural health monitoring for a benchmark structure using adaptive RLS filters Computers & Structures, Volume 83, Issues 8-9, March 2005, Pages 639-647 J. Geoffrey Chase, Vincent Begoc, Luciana R. Barroso
4. Parameter estimation of the generalized extreme value distribution for structural health monitoring Probabilistic Engineering Mechanics, Volume 21, Issue 4, October 2006, Pages 366-376 Hyun Woo Park, Hoon Sohn
5. Fibre Bragg gratings in structural health monitoring—Present status and applicationsSensors and Actuators A: Physical, Volume 147, Issue 1, 15 September 2008, Pages 150-164Mousumi Majumder, Tarun Kumar Gangopadhyay, Ashim Kumar Chakraborty, Kamal Dasgupta, D.K. Bhattacharya
6. A methodology for structural health monitoring with diffuse ultrasonic waves in the presence of temperature variations Ultrasonics, Volume 43, Issue 9, October 2005, Pages 717-731Yinghui Lu, Jennifer E. Michaels
7. Fiber optic acoustic emission sensor and its applications in the structural health monitoring of CFRP materials Optics and Lasers in Engineering, In Press, Corrected Proof, Available online 14 July 2009 Tao Fu, Yanju Liu, Quanlong Li, Jinsong Leng
8. Towards a practical structural health monitoring technology for patched cracks in aircraft structure Composites Part A: Applied Science and Manufacturing, In Press, Corrected Proof, Available online 30 September 2008 Alan Baker, Nik Rajic, Claire Davis
9. Accurate high-resolution fiber-optic distributed strain measurements for structural health monitoring Sensors and Actuators A: Physical, Volume 134, Issue 2, 15 March 2007, Pages 389-395 Romeo Bernini, Aldo Minardo and, Luigi Zeni
10. Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation Engineering Structures, Volume 28, Issue 5, April 2006, Pages 648-659 T.H.T. Chan, L. Yu, H.Y. Tam, Y.Q. Ni, S.Y. Liu, W.H. Chung, L.K. Cheng
11. Structural health monitoring of smart composite materials by using EFPI and FBG sensorsSensors and Actuators A: Physical, Volume 103, Issue 3, 15 February 2003, Pages 330-340Jinsong Leng, Anand Asundi
12. Structural health monitoring of a composite bridge using Bragg grating sensors. Part 1: Evaluation of adhesives and protection systems for the optical sensors Engineering Structures, Volume 29, Issue 3, March 2007, Pages 440-448 G. Kister, D. Winter, R.A. Badcock, Y.M. Gebremichael, W.J.O. Boyle, B.T. Meggitt, K.T.V. Grattan, G.F. Fernando
13. Equilibrium point damage prognosis models for structural health monitoring Journal of Sound and Vibration, Volume 262, Issue 3, 1 May 2003, Pages 591-611 Rebecca L. Brown, Douglas E. Adams
14. Structural health monitoring for flexible bridge structures using correlation and sensitivity of modal data Computers & Structures, Volume 85, Issues 3-4, February 2007, Pages 117-130 B.H. Koh, S.J. Dyke

Review

2009-08-03T08:39:00.000-07:00

Johnson et al [1] presented work on smart sensor arrays for distributed structural health monitoring and damage diagnosis. The goal of their work was to implement local vibration-based diagnostic algorithms inside a smart ‘black box’ to demonstrate the feasibility of distributed health monitoring for damage detection and location. Dynamic transmissibility features for SHM and the smart-processing platform have been described in detail and various damage configurations in two large test structures, a representative three-storey building and a rotorcraft fuselage, have been diagnosed. Their results showed that the near real-time integrated monitoring system works well in spite of certain limited environmental fluctuations (e.g. temperature, input levels) and boundary condition non-linearity. Wired piezoelectric arrays of accelerometers are implemented in conjunction with the black box.
To deal with the diverse, heterogeneous and distributed information obtained by density or different kinds of sensors at different sites on a large scale engineering structure, multi-agent system (MAS) based structural health monitoring (SHM) technology have been adopted by Zhao et al [2] with a general framework considering function design, ontology design, individual agent design, facilitator design, society behavior and learning behavior design which can be plugged in any multi-agent based SHM system and tested with a case study.
Zhao et al [3] introduced an evaluation on a multi-agent system based structural health monitoring to validate the efficiency of the multi-agent technology, which can automatically choose sensing object, can self-organize the sensor network and discard useless sensor data, and can recognize three typical kinds of structure states which may indicate structural damages including impact load, joint failure, strain distribution change on every substructure and the edge area between two adjacent substructures.
Wu et al [4] presented a multi-agent design method and system evaluation for wireless sensor network based structural health monitoring including the wireless strain gauge node, wireless PZT node and wireless USB station which can automatically allocate SHM tasks; self-organize the sensor network and aggregate different sensor information using six different SHM agents.
Son et al [5] considered support vector machine, linear discriminate analysis, k-nearest neighbors, and random forests algorithm as classifiers for fault diagnosis of machine using three types of signals involving vibration, current, and flux from induction motors.
Wang et al [6] identified a through-thickness hole in a stiffened composite panel using guided wave-based damage diagnostic algorithm, which is based on the probability of the presence of damage in the monitoring area estimated using correlation coefficients of Lamb wave signals from an active sensor network.
Shah and Ribakov [7] presented findings on nonlinear ultrasonic testing of concrete with different frequency transducers. As concrete is a high attenuation material, the change in fundamental amplitude or attenuation was found sensitive to using transducers with different frequency. The test results were also analyzed to find correlation between w/c, wave attenuation and higher harmonic generation based nonlinear parameters.

Overbey and Todd [8] evaluated input and output noise sensitivity on transfer entropy damage index as a feature for nonlinearity detection and linear damage identification, which requires the estimation of non-parametric one-, two-, and three-dimensional probability density functions.
Gul and Catbas [9] investigated statistical pattern recognition methods in the context of SHM using time series modeling, i.e. auto-regressive models, in conjunction with Mahalanobis distance-based outlier detection algorithms to identify different types of structural changes on different test structures.
Lopez and Klijn [10] used distance similarity matrix of dimensionally reduced data and shown ensembles perform better than any single-feature extraction method.
1. Distributed structural health monitoring with a smart sensor arrayMechanical Systems and Signal Processing, Volume 18, Issue 3, May 2004, Pages 555-572Timothy J. Johnson, Rebecca L. Brown, Douglas E. Adams, Mark Schiefer
2. Designing strategy for multi-agent system based large structural health monitoringExpert Systems with Applications, Volume 34, Issue 2, February 2008, Pages 1154-1168Xia Zhao, Shenfang Yuan, Zhenhua Yu, Weisong Ye, Jun Cao
3. An evaluation on the multi-agent system based structural health monitoring for large scale structures Expert Systems with Applications, Volume 36, Issue 3, Part 1, April 2009, Pages 4900-4914 Xia Zhao, Shenfang Yuan, Hengbao Zhou, Hongbing Sun, Lei Qiu
4. Multi-agent system design and evaluation for collaborative wireless sensor network in large structure health monitoring Expert Systems with Applications, In Press, Corrected Proof, Available online 7 July 2009 Jian Wu, Shenfang Yuan, Sai Ji, Genyuan Zhou, Yang Wang, Zilong Wang
5. Development of smart sensors system for machine fault diagnosisExpert Systems with Applications, Volume 36, Issue 9, November 2009, Pages 11981-11991Jong-Duk Son, Gang Niu, Bo-Suk Yang, Don-Ha Hwang, Dong-Sik Kang
6. Probability of the presence of damage estimated from an active sensor network in a composite panel of multiple stiffenersComposites Science and Technology, Volume 69, Issue 13, October 2009, Pages 2054-2063Dong Wang, Lin Ye, Ye Lu, Zhongqing Su
7. Non-destructive evaluation of concrete in damaged and undamaged statesMaterials & Design, Volume 30, Issue 9, October 2009, Pages 3504-3511A.A. Shah, Y. Ribakov
8. Effects of noise on transfer entropy estimation for damage detectionMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2178-2191L.A. Overbey, M.D. Todd
9. Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verificationsMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2192-2204Mustafa Gul, F. Necati Catbas
10. Distance similarity matrix using ensemble of dimensional data reduction techniques: Vibration and aerocoustic case studiesMechanical Systems and Signal Processing, Volume 23, Issue 7, October 2009, Pages 2287-2300Israel Lopez, Nesrin Sarigul-Klijn

Early Warning System for Detecting Fatigue Cracks in Aircraft

2007-05-03T21:32:00.000-07:00

Today's aircraft must operate reliably and safely at all times. An active sensor/actuator system helps to detect concealed fatigue cracks in aircraft bodies, thus reducing maintenance times considerably.
If a tiny crack in the hull of an aircraft remains undetected for a long time, it can easily spread and put passengers’ lives at risk. Each component is therefore subject to international maintenance regulations: Aircraft bodies must be thoroughly inspected every 15 to 18 months, depending on the type of plane. This often requires the technicians to dismantle an aircraft’s interior lining – an expensive and time-consuming process involving several days of downtime in the hangar.
If a component is defective, it oscillates at a different frequency from one that is intact when stimulated by the actuator– just as a cracked glass sounds different from an undamaged one when you tap it with your fingernail. In this way, the researchers can accurately track down faults and monitor their development – be it fatigue cracks in the hull and wings, or rivets and other joining parts coming loose. This form of observation is referred to as ‘structural health monitoring’ (SHM).
With the help of this system, any damage behind the lining or in difficult-to-reach places can be identified quickly and easily, thus reducing maintenance times and inspection costs. The system will then also be suitable for use in vehicle manufacture or engine and plant construction.

This is Test

2007-02-08T03:25:00.000-08:00

Structural health monitoring