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

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.