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contributor authorY. Q. Ni
contributor authorH. F. Zhou
contributor authorJ. M. Ko
date accessioned2017-05-08T21:58:51Z
date available2017-05-08T21:58:51Z
date copyrightOctober 2009
date issued2009
identifier other%28asce%29st%2E1943-541x%2E0000091.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/67936
description abstractThe 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.
publisherAmerican Society of Civil Engineers
titleGeneralization Capability of Neural Network Models for Temperature-Frequency Correlation Using Monitoring Data
typeJournal Paper
journal volume135
journal issue10
journal titleJournal of Structural Engineering
identifier doi10.1061/(ASCE)ST.1943-541X.0000050
treeJournal of Structural Engineering:;2009:;Volume ( 135 ):;issue: 010
contenttypeFulltext


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