| Title |
Hierarchical wavelet-aided neural intelligent identification of structural damage in noisy conditions / |
| Authors |
Cao, Mao-Sen ; Ding, Yu-Juan ; Ren, Wei-Xin ; Wang, Quan ; Ragulskis, Minvydas ; Ding, Zhi-Chun |
| DOI |
10.3390/app7040391 |
| Full Text |
|
| Is Part of |
Applied sciences.. Basel : MDPI. 2017, vol. 7, iss. 4, art. no. 391, p. 1-20.. ISSN 2076-3417 |
| Keywords [eng] |
auto-associative neural network ; Levenberg-Marquardt neural network ; wavelet packet transform ; damage feature extraction ; damage assessment ; nonlinear principal component analysis ; bridge structure |
| Abstract [eng] |
A sophisticated hierarchical neural network model for intelligent assessment of structural damage is constructed by the synergetic action of auto-associative neural networks (AANNs) and Levenberg-Marquardt neural networks (LMNNs). With the model, AANNs aided by the wavelet packet transform are firstly employed to extract damage features from measured dynamic responses and LMNNs are then utilized to undertake damage pattern recognition. The synergetic functions endow the model with a unique mechanism of intelligent damage identification in structures. The model is applied for the identification of damage in a three-span continuous bridge, with particular emphasis on noise interference. The results show that the AANNs can produce a low-dimensional space of damage features, from which LMNNs can recognize both the location and the severity of structural damage with great accuracy and strong robustness against noise. The proposed model holds promise for developing viable intelligent damage identification technology for actual engineering structures. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2017 |
| CC license |
|
