Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33310
Title: Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles
Authors: Li, J
Dackermann, U
Xu, YL 
Samali, B
Keywords: Artificial neural network
Damage identification
Frequency response functions
Neural network ensemble
Principal component analysis
Structural health monitoring
Issue Date: 2011
Source: Structural control and health monitoring, 2011, v. 18, no. 2, p. 207-226 How to cite?
Journal: Structural Control and Health Monitoring 
Abstract: This paper presents a non-destructive, global, vibration-based damage identification method that utilizes damage pattern changes in frequency response functions (FRFs) and artificial neural networks (ANNs) to identify defects. To extract damage features and to obtain suitable input parameters for ANNs, principal component analysis (PCA) techniques are applied. Residual FRFs, which are the differences in the FRF data from the intact and the damaged structure, are compressed to a few principal components and fed to ANNs to estimate the locations and severities of structural damage. A hierarchy of neural network ensembles is created to take advantage of individual information from sensor signals. To simulate field-testing conditions, white Gaussian noise is added to the numerical data and a noise sensitivity study is conducted to investigate the robustness of the developed damage detection technique to noise. Both numerical and experimental results of simply supported steel beam structures have been used to demonstrate effectiveness and reliability of the proposed method.
URI: http://hdl.handle.net/10397/33310
ISSN: 1545-2255
DOI: 10.1002/stc.369
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