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

Tools

Dang, Hung V., Raza, Mohsin, Tran-Ngoc, H., Bui-Tien, T. and Nguyen, Huan X. (2021) Connection stiffness reduction analysis in steel bridge via deep CNN and modal experimental data. Structural Engineering and Mechanics, 77 (4). pp. 495-508. ISSN 1225-4568

Full text not available from this repository. (Request a copy)

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%.

Item Type:	Article
Additional Information:	Publisher Copyright: Copyright © 2021 Techno-Press, Ltd.
Uncontrolled Keywords:	convolutional neural networks,damage detection and localization,machine learning,numerical simulation,steel truss bridge,structural monitoring,vibration,civil and structural engineering,building and construction,mechanics of materials,mechanical engineering ,/dk/atira/pure/subjectarea/asjc/2200/2205
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	18 Jun 2025 14:30
Last Modified:	18 Jun 2025 14:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/99634
DOI:	10.12989/sem.2021.77.4.495

Actions (login required)

View Item