期刊
ADVANCES IN STRUCTURAL ENGINEERING
卷 13, 期 6, 页码 1001-1016出版社
SAGE PUBLICATIONS INC
DOI: 10.1260/1369-4332.13.6.1001
关键词
damage identification; artificial neural network; neural network ensemble; structural health monitoring; damage index method; modal strain energy; principal component analysis
资金
- Centre for Built Infrastructure Research (CBIR)
- Faculty of Engineering and Information Technology (FEIT)
- University of Technology Sydney (UTS)
This paper presents a vibration-based damage identification method that utilises a damage fingerprint of a structure in combination with Principal Component Analysis (PCA) and neural network techniques to identify defects. The Damage Index (DI) method is used to extract unique damage patterns from a damaged beam structure with the undamaged structure as baseline. PCA is applied to reduce the effect of measurement noise and optimise neural network training. PCA-compressed DI values are, then, used as inputs for a hierarchy of neural network ensembles to estimate locations and seventies of various damage cases. The developed method is verified by a laboratory structure and numerical simulations in which measurement noise is taken into account with different levels of white Gaussian noise added. The damage identification results obtained from the neural network ensembles show that the presented method is capable of overcoming problems inherent in the conventional DI method. Issues associated with field testing conditions are successfully dealt with for numerical and the experimental simulations. Moreover, it is shown that the neural network ensemble produces results that are more accurate than any of the outcomes of the individual neural networks.
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