Structural Health Monitoring and Damage Detection in Steel Bridges Using AI-Based Vibration Analysis

Structural Health Monitoring and Damage Detection in Steel Bridges Using AI-Based Vibration Analysis | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL The Journal of Engineering This is a preprint and has not been peer reviewed. Data may be preliminary. 13 May 2026 V1 Latest version Share on Structural Health Monitoring and Damage Detection in Steel Bridges Using AI-Based Vibration Analysis Authors : Yongguo Guo 0009-0002-7349-1659 [email protected] , Yanbo Yu [email protected] , and Xiaojian Xu [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003287/v1 24 views 19 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Structural Health Monitoring (SHM) is a significant indicator of safety and reliability in steel bridges, which endure dynamic traffic loads, environmental impacts, and material degradation. In this study, introduce a novel artificial intelligence-based framework for vibration analysis, which leverages DenseNet-121, Convolutional Neural Network-Bidirectional Long Short-term Memory (CNN-BiLSTM) and Sharpness-Aware Minimization (SAM) optimization which enhances accuracy for bridge damage characterization and generalization. This processes the multi-sensor vibrations data with preprocessing steps, including band-pass filtering, wavelet denoising and normalization, enabling the removal of noise or interference and stabilization of the signals. Both the deep spatial and deep temporal features have the ability to learn to extract, evolve and fuses the features to delineate structural condition from healthy to severely damaged. An experimental evaluation of the Bridge Vibration Dataset from Zenodo indicates hover performance with a accuracy of 98.08%, recall of 97.58%, and precision of 98.68%, outperforming conventional and recent deep learning models. The findings confirm the robustness of new methods under noisy and inconstant conditions in preparation for more real-time and easy bridge monitoring, predictive maintenance. Overall, the study presents intelligent SHM systems that can provide safe, data-driven, and cost-effective approaches to infrastructure management. Information & Authors Information Version history V1 Version 1 13 May 2026 Collection The Journal of Engineering Keywords artificial intelligence analytical model control engineering Dynamics and control Control engineering and robotics artificial intelligence Big Data 3D imaging 3D modelling 3D printing 3D video control engineering Dynamics and control Control engineering and robotics artificial intelligence analytical model Authors Affiliations Yongguo Guo 0009-0002-7349-1659 [email protected] China Highway Engineering Consulting Group Co., Ltd., Beijing, China, 100089 View all articles by this author Yanbo Yu [email protected] China Highway Engineering Consulting Group Co., Ltd., Beijing, China, 100089 View all articles by this author Xiaojian Xu [email protected] China Highway Engineering Consulting Group Co., Ltd., Beijing, China, 100089 View all articles by this author Metrics & Citations Metrics Article Usage 24 views 19 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yongguo Guo, Yanbo Yu, Xiaojian Xu. Structural Health Monitoring and Damage Detection in Steel Bridges Using AI-Based Vibration Analysis. Authorea . 13 May 2026. DOI: https://doi.org/10.22541/authorea.15003287/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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