Generalization Capability of Neural Network Models for Temperature-Frequency Correlation Using Monitoring Data
Publication: Journal of Structural Engineering
Volume 135, Issue 10
Abstract
The parametric approach to eliminating the temperature-caused modal variability in vibration-based structural damage detection requires a correlation model between the modal properties and environmental temperatures. This paper examines the generalization capability of neural network models, established using long-term monitoring data, for correlation between the modal frequencies and environmental temperatures. A total of 770 h modal frequency and temperature data obtained from an instrumented bridge are available for this study, which are further divided into three sets: training data, validation data, and testing data. A two-layer back-propagation neural network (BPNN) is first trained using the training data by the conventional training algorithm, in which the number of hidden nodes is optimally determined using the validation data. Then two new BPNNs are configured with the same data by applying the early stopping technique and the Bayesian regularization technique, respectively. The reproduction and prediction capabilities of the two new BPNNs are examined in respect of the training data and the unseen testing data, and compared with the performance of the baseline BPNN model. This study indicates that both the early stopping and Bayesian regularization techniques can significantly ameliorate the generalization capability of BPNN-based correlation models, and the BPNN model formulated using the early stopping technique outperforms that using the Bayesian regularization technique in both reproduction and prediction capabilities.
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Acknowledgments
The work described in this paper was supported in part by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Grant No. UNSPECIFIEDPolyU 5142/04E) and partially by a grant from The Hong Kong Polytechnic University through the Development of Niche Areas Program (Project No. UNSPECIFIED1-BB68). The writers also thank the Hong Kong SAR Government Highways Department for providing support to this research.
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Information
Published In
Journal of Structural Engineering
Volume 135 • Issue 10 • October 2009
Pages: 1290 - 1300
Copyright
© 2009 ASCE.
History
Received: Aug 8, 2008
Accepted: Mar 1, 2009
Published online: Mar 26, 2009
Published in print: Oct 2009
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