Journal
JOURNAL OF BRIDGE ENGINEERING
Volume 25, Issue 4, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)BE.1943-5592.0001531
Keywords
Bridge structural damage; Fiber optic sensing; Convolutional neural networks; Supervised learning
Categories
Funding
- National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology
- Smart Nanocomposites Laboratory, University of California, Irvine, CA
- National Natural Science Foundation of China [61875155, 61735013]
- Fundamental Research Funds for the Central Universities [WUT: 2019-III-160CG]
- China Scholarship Council (CSC)
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Improving the accuracy and efficiency of damage detection of bridge structures is a major challenge in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic sensing technology and applying a deep-learning algorithm to perform structural damage detection. With a scaled-down bridge model, three categories of damage scenarios plus an intact state were simulated. A 13-layer supervised learning model based on the deep convolutional neural networks was proposed. After the training process of original continuous deflection under 10-fold cross-validation, the model accuracy can reach 96.9% for damage classification with the performance outperforming that of the other four methods (random forest = 81.6%, support vector machine = 79.9%, k-nearest neighbor = 77.7%, and decision tree = 74.8%). The proposed model also demonstrated its decent abilities in automatically extracting damage features and distinguishing damage from structurally symmetrical locations. (c) 2020 American Society of Civil Engineers.
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