Identification of damage in dome-like structures using hybrid sensor measurements and artificial neural networks

A damage detection scheme using multi-type sensor-based hybrid sensing and artificial-neural-network- (ANN-) based information processing was developed for dome-like structures used in civil infrastructure. Accelerometers and strain sensors were used to provide a hybrid measurement with the purpose of acquiring rich information associated with structural damage. The optimal placement of multiple sensors was explored so as to capture the most appropriate and sensitive signal features (damage parameter vectors) for damage characterization. A back-propagation ANN was constructed with the inputs extracted from the hybrid measurement. To validate the capacity of the proposed damage identification scheme, finite element analysis was conducted to identify damage in a Schwedler dome structure as an example. The performance of ANNs, trained by three kinds of damage parameter vector extracted from signals captured by (i) a sole accelerometer, (ii) a sole strain sensor, and (iii) both kinds of sensor was compared, to observe that the one trained by hybrid sensor measurement outperformed the others. Error analysis for a series of parametric studies, in which noise at different levels was included in the training input, was further carried out, and robustness of the proposed damage identification scheme under noisy measurement was demonstrated.

[1]  Damodar Maity,et al.  Damage assessment in structure from changes in static parameter using neural networks , 2004 .

[2]  Hui Li,et al.  Structural damage identification based on integration of information fusion and shannon entropy , 2008 .

[3]  W. T. Yeung,et al.  Damage detection in bridges using neural networks for pattern recognition of vibration signatures , 2005 .

[4]  M. L. Wang,et al.  Structural Damage Detection Using an Iterative Neural Network , 2000 .

[5]  Yi-Qing Ni,et al.  Constructing input vectors to neural networks for structural damage identification , 2002 .

[6]  José L. Zapico,et al.  Seismic damage identification in buildings using neural networks and modal data , 2008 .

[7]  Yuyin Qian,et al.  Acceleration-based damage indicators for building structures using neural network emulators , 2008 .

[8]  L. Ye,et al.  Lamb Wave Propagation-based Damage Identification for Quasi-isotropic CF/EP Composite Laminates Using Artificial Neural Algorithm: Part II - Implementation and Validation , 2005 .

[9]  Jin-Hak Yi,et al.  Sequential damage detection approaches for beams using time-modal features and artificial neural networks , 2009 .

[10]  Mauro Serpelloni,et al.  Kinetic and thermal energy harvesters for implantable medical devices and biomedical autonomous sensors , 2013 .

[11]  Yl L. Xu,et al.  Integrated vibration control and health monitoring of building structures using semi-active friction dampers: Part II — Numerical investigation , 2008 .

[12]  Gao Wei-cheng.  NUMERICAL AND EXPERIMENTAL STUDY ON DAMAGE DETECTION OF RETICULATED SHELL , 2006 .

[13]  Yu Yang,et al.  Study on the structural damage identification method with combined parameters based on RBF neural network , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[14]  Y Q Lin,et al.  Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory , 2011 .

[15]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[16]  John N. Ivan,et al.  Structural Damage Detection Using Artificial Neural Networks , 1998 .

[17]  Deh-Shiu Hsu,et al.  Diagnosis of Reinforced Concrete Structural Damage Base on Displacement Time History Using the Back-Propagation Neural Network Technique , 2002 .