A supervised machine learning approach for structural overload classification in railway bridges using weigh-in-motion data

Weigh-in-motion (WIM) data provides valuable information on vehicle axle load, enabling efficient and economical railway structural safety management programs. However, the current method for assessing structural overload on railway bridges using WIM data is time-consuming and often requires line closure while analyses are being conducted. This paper presents the development of a novel supervised machine learning (ML) approach that can be used as an assessment tool to expedite the decision-making process and minimise economic loss. Variables for model input are carefully considered by analysing real WIM data obtained from measurement sites in New Zealand. Various supervised ML classification models are evaluated for their capability in classifying axle load combinations (ALC) into “Normal”, meaning safe to go, or “Overload”, meaning that line closure is required for detailed inspection of the affected bridges. It is found that the model using Neural Network (NN) outperforms other candidates in this capacity and is therefore selected for detailed model development. An initial investigation using a small dataset derived from real WIM measurements demonstrates that the NN model can achieve impressive evaluation metrics such as F1-score of 99.2 %. Subsequently, a method for artificially generating synthetic ALC data is proposed to create extensive training datasets for comprehensive structural overload model development. It is demonstrated that with sufficient overload data in the training dataset, the model can achieve an exceptional performance, reaching an F1-score of 99.84 % or higher for a single overload level and 99.5 % to 99.86 % for multiple overload thresholds. The developed model can be integrated into the WIM post-processing systems, providing a real-time bridge overload assessment tool that facilitates more efficient and cost-effective railway structural safety management.