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.
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
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
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