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http://dx.doi.org/10.12989/sem.2021.77.4.495

Connection stiffness reduction analysis in steel bridge via deep CNN and modal experimental data  

Dang, Hung V. (Faculty of Science and Technology, Middlesex University)
Raza, Mohsin (Faculty of Science and Technology, Middlesex University)
Tran-Ngoc, H. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications)
Bui-Tien, T. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications)
Nguyen, Huan X. (Faculty of Science and Technology, Middlesex University)
Publication Information
Structural Engineering and Mechanics / v.77, no.4, 2021 , pp. 495-508 More about this Journal
Abstract
This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.
Keywords
structural monitoring; machine learning; steel truss bridge; vibration; numerical simulation; damage detection and localization; convolutional neural networks;
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