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J.A. Sainz-Aja¹, D. Ferreño¹, J. Pombo^2,3, I. Carrascal¹, J. Casado¹, S. Diego¹, J. Castro¹ and I. Rivas¹

¹University of Cantabria, Spain
²Institute of Railway Research, School of Computing and Engineering, University of Huddersfield, UK
³IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

doi:10.4203/ccc.7.8.13

J.A. Sainz-Aja, D. Ferreño, J. Pombo, I. Carrascal, J. Casado, S. Diego, J. Castro, I. Rivas, "Machine Learning-Based Parametric Analysis of Railway Systems", 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 8.13, 2024, doi:10.4203/ccc.7.8.13

Keywords: railway tracks, infrastructure assets, predictive models, machine learning algorithms, Monte Carlo method, predictive maintenance.

Abstract

Effective and meticulous management of railway infrastructure is essential to prevent accidents and minimize operation and maintenance costs. This requires comprehensive knowledge of the assets, their interactions, and the impact of each track parameter on the overall performance of the infrastructure. This study conducts extensive analyses using a previously calibrated finite element model of slab track, varying key track parameters within their typical ranges. The resulting data is then used to train and validate predictive models employing machine learning algorithms. This approach provides deeper insights and improves the prediction of track behavior, which involves numerous variables such as soil/subgrade, supporting layers, sleepers, pads, and rails. Additionally, the study considers the impact of train axle loads and service speeds, which are crucial factors affecting track performance. The findings highlight that the most influential parameters on railway infrastructure are soil properties, rail pad characteristics, and axle loads. This research can facilitate the implementation of predictive maintenance strategies for railway tracks and the development of innovative technological solutions, addressing industrial needs for cost reduction and enhancing the competitiveness of railway transport.

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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 8.13 Machine Learning-Based Parametric Analysis of Railway Systems J.A. Sainz-Aja¹, D. Ferreño¹, J. Pombo^2,3, I. Carrascal¹, J. Casado¹, S. Diego¹, J. Castro¹ and I. Rivas¹ ¹University of Cantabria, Spain ²Institute of Railway Research, School of Computing and Engineering, University of Huddersfield, UK ³IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal doi:10.4203/ccc.7.8.13 Full Bibliographic Reference for this paper J.A. Sainz-Aja, D. Ferreño, J. Pombo, I. Carrascal, J. Casado, S. Diego, J. Castro, I. Rivas, "Machine Learning-Based Parametric Analysis of Railway Systems", 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 8.13, 2024, doi:10.4203/ccc.7.8.13 Keywords: railway tracks, infrastructure assets, predictive models, machine learning algorithms, Monte Carlo method, predictive maintenance. Abstract Effective and meticulous management of railway infrastructure is essential to prevent accidents and minimize operation and maintenance costs. This requires comprehensive knowledge of the assets, their interactions, and the impact of each track parameter on the overall performance of the infrastructure. This study conducts extensive analyses using a previously calibrated finite element model of slab track, varying key track parameters within their typical ranges. The resulting data is then used to train and validate predictive models employing machine learning algorithms. This approach provides deeper insights and improves the prediction of track behavior, which involves numerous variables such as soil/subgrade, supporting layers, sleepers, pads, and rails. Additionally, the study considers the impact of train axle loads and service speeds, which are crucial factors affecting track performance. The findings highlight that the most influential parameters on railway infrastructure are soil properties, rail pad characteristics, and axle loads. This research can facilitate the implementation of predictive maintenance strategies for railway tracks and the development of innovative technological solutions, addressing industrial needs for cost reduction and enhancing the competitiveness of railway transport. download the full-text of this paper (PDF, 10 pages, 856 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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