Harnessing Machine Learning for Railway Damage Assessment and Prediction

Railway systems in coastal regions face distinct challenges due to environmental factors such as coastal erosion, hurricanes, and rising sea levels. Coastal erosion undermines railway embankments, resulting in structural instability and heightened maintenance costs. Hurricanes and storm surges present further risks, with high winds and flooding threatening to damage rail infrastructure, disrupt services, and compromise public safety. To address these vulnerabilities, railway systems in these areas require advanced engineering solutions capable of mitigating the effects of extreme weather events while ensuring operational continuity. This thesis seeks to leverage machine learning techniques to assess and quantify damage to railway infrastructure following major storm events, providing valuable insights to inform the design and maintenance of coastal railway systems. By collecting and processing data on railway characteristics, storm events, and resulting damage, a predictive machine learning model was developed to estimate future storm-related damage. The model achieved an accuracy of approximately 84%, demonstrating its potential for practical application. Additionally, three hypothetical scenarios were developed to evaluate the model's performance by varying key input parameters. The findings of this study offer critical guidance for railway stakeholders in making informed decisions regarding the reinforcement of coastal railways and pave the way for further research and model refinement.