Machine Learning Based Predictive Modelling of a Steel Railway Bridge for Damage Modelling of Train Passages and Different Usage Scenarios

Onderzoeksoutput: Conference paperResearch


Railway bridges are key assets of a countries’ infrastructure, enabling transport of goods and people through freight and passenger trains. The studied structure is a steel railway bridge subjected to cyclic loading, equipped with 98 Fiber Bragg Gratings. A previous study identified train passages as main drivers of damage, isolated and converted them to fatigue damage. This research aims at predicting this damage through machine learning with available operational data as input (train type, train speed, ...) and adding publicly available data (temperature, humidity, ...). The research uses 4 months’ data of train passages and focuses on passenger trains, as too few freight train passages were recorded. Random Forest regression was selected for its ease of implementation with categorical data and high R-squared score. A model was trained for every sensor point. Additionally, the model classifies sensors based on damage predictability. Finally, the models were used to determine long-term damage caused by different bridge loading scenarios. By fixing a parameter like train type and then randomly sampling from train passages, the remaining train passages until a damage threshold is reached are estimated. By repeating this simulation 1000 times for every scenario, remaining train passages distributions are reached, showing best and worst case estimates.
Originele taal-2English
TitelEuropean Workshop on Structural Health Monitoring
SubtitelEWSHM 2022 - Volume 3
RedacteurenPiervincenzo Rizzo, Alberto Milazzo
Aantal pagina's10
ISBN van geprinte versie9783031073212
StatusPublished - 22 jun 2022
EvenementEuropean workshop on Structural Health Monitoring (2022) - Palermo, Italy
Duur: 4 jul 20227 jul 2022

Publicatie series

NaamLecture Notes in Civil Engineering


ConferenceEuropean workshop on Structural Health Monitoring (2022)
Verkorte titelEWSHM 2022

Bibliografische nota

Funding Information:
Acknowledgement. This research is being conducted within the project ICON SafeLife-INFRABEL under the title of “Lifetime prediction and management of fatigue loaded welded steel structures based on structural health monitoring”, funded by VLAIO (Agentschap Innoveren & Ondernemen).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright 2022 Elsevier B.V., All rights reserved.


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