Computational Technology Resources - CCC

C. Braganca¹, E.D. De Souza^1,2, D. Ribeiro³ and T. Bittencourt¹

¹Department of Structural and Geotechnical Engineering, University of São Paulo, Brazil
²School of Engineering, Federal Technological University of Paraná, Brazil
³CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal

doi:10.4203/ccc.7.15.7

C. Braganca, E.D. De Souza, D. Ribeiro, T. Bittencourt, "Drive-By Early Damage Detection in Railway Bridges using Wavelets and Autoencoders", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 15.7, 2024, doi:10.4203/ccc.7.15.7

Keywords: drive-by, damage identification, vehicle-structure interaction, wavelet scattering transform, autoencoders, data fusion.

Abstract

This paper presents an innovative AI-driven drive-by methodology for unsupervised damage detection on a Warren truss bridge. The methodology employs acceleration data collected from eight sensors mounted on a LAAGRSS-type freight wagon. Wavelet scattering coefficients derived from these acceleration signals serve as input features for the model. Autoencoders, trained on baseline condition data, are utilized to reconstruct these coefficients, with the absolute reconstruction error acting as a damage-sensitive feature. Environmental and operational variations are mitigated through normalization, excluding high-variability components. A three-level data fusion approach, based on the Mahalanobis distance, generates a highly sensitive damage indicator. This indicator accurately detects all simulated damage scenarios, including those in their early stages, without misclassification. The study demonstrates the efficacy of the proposed methodology also for distinguishing between different damage types. Future work will focus on experimental validation and enhancement of the methodology for assessing damage severity.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 15.7 Drive-By Early Damage Detection in Railway Bridges using Wavelets and Autoencoders C. Braganca¹, E.D. De Souza^1,2, D. Ribeiro³ and T. Bittencourt¹ ¹Department of Structural and Geotechnical Engineering, University of São Paulo, Brazil ²School of Engineering, Federal Technological University of Paraná, Brazil ³CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal doi:10.4203/ccc.7.15.7 Full Bibliographic Reference for this paper C. Braganca, E.D. De Souza, D. Ribeiro, T. Bittencourt, "Drive-By Early Damage Detection in Railway Bridges using Wavelets and Autoencoders", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 15.7, 2024, doi:10.4203/ccc.7.15.7 Keywords: drive-by, damage identification, vehicle-structure interaction, wavelet scattering transform, autoencoders, data fusion. Abstract This paper presents an innovative AI-driven drive-by methodology for unsupervised damage detection on a Warren truss bridge. The methodology employs acceleration data collected from eight sensors mounted on a LAAGRSS-type freight wagon. Wavelet scattering coefficients derived from these acceleration signals serve as input features for the model. Autoencoders, trained on baseline condition data, are utilized to reconstruct these coefficients, with the absolute reconstruction error acting as a damage-sensitive feature. Environmental and operational variations are mitigated through normalization, excluding high-variability components. A three-level data fusion approach, based on the Mahalanobis distance, generates a highly sensitive damage indicator. This indicator accurately detects all simulated damage scenarios, including those in their early stages, without misclassification. The study demonstrates the efficacy of the proposed methodology also for distinguishing between different damage types. Future work will focus on experimental validation and enhancement of the methodology for assessing damage severity. download the full-text of this paper (PDF, 12 pages, 1414 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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