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.