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
Monday, August 17, 2009
review of SHM
Thursday, August 13, 2009
SHM review
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
Wednesday, August 12, 2009
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.
Monday, August 10, 2009
review of SHM
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.
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
Friday, August 7, 2009
Review
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
Thursday, August 6, 2009
Review
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
Monday, August 3, 2009
Review
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
