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Appl. Sci. 2017, 7(4), 391; doi:10.3390/app7040391

Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions

1
Department of Engineering Mechanics, Hohai University, Nanjing 210098, China
2
School of Civil Engineering, Hefei University of Technology, Hefei 230009, China
3
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR
4
Research Group for Mathematical and Numerical Analysis of Dynamical Systems, Kaunas University of Technology, Kaunas LT-51368, Lithuania
*
Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Lacidogna
Received: 20 January 2017 / Revised: 4 April 2017 / Accepted: 5 April 2017 / Published: 14 April 2017
(This article belongs to the Special Issue Structural Health Monitoring (SHM) of Civil Structures)
View Full-Text   |   Download PDF [2593 KB, uploaded 14 April 2017]   |  

Abstract

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. View Full-Text
Keywords: auto-associative neural network; Levenberg-Marquardt neural network; wavelet packet transform; damage feature extraction; damage assessment; nonlinear principal component analysis; bridge structure auto-associative neural network; Levenberg-Marquardt neural network; wavelet packet transform; damage feature extraction; damage assessment; nonlinear principal component analysis; bridge structure
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cao, M.-S.; Ding, Y.-J.; Ren, W.-X.; Wang, Q.; Ragulskis, M.; Ding, Z.-C. Hierarchical Wavelet-Aided Neural Intelligent Identification of Structural Damage in Noisy Conditions. Appl. Sci. 2017, 7, 391.

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