Intelligent Monitoring of Longitudinal Rail Force Using Time Series Forecasting Large Language Models

ABSTRACT High-speed railways constitute a critical component of modern transportation infrastructure. However, the longitudinal rail force, influenced by cyclic train loads and environmental conditions, poses considerable safety risks if not accurately monitored. Traditional longitudinal rail force detection techniques primarily rely on physical testing and periodic manual inspections, which significantly limit the potential for real-time and continuous monitoring. To address these limitations, this study introduces an innovative approach employing large language models (LLM) to predict longitudinal rail force based on historical monitoring data. The proposed method is validated through extensive long-term field monitoring of longitudinal rail force on high-speed railway lines, thereby confirming its practical applicability. In contrast to conventional Time Series Forecasting Large Language Models (Time-LLM), the proposed method evaluates the prompt-free architecture. When applied to real-world longitudinal rail force data from the Beijing-Shanghai High-Speed Railway, the model achieves an average coefficient of determination (R 2 ) of 0.932 and a root mean square error (RMSE) of 3.537 kN, outperforming traditional deep learning models. Furthermore, the model exhibits strong robustness under conditions of intermittent data loss. The proposed framework is seamlessly integrated into a localized intelligent system using Langchain-Chatchat, enabling expert-level recommendations based on domain-specific documentation. Overall, this study presents a practical, efficient, and scalable solution for intelligent railway monitoring, offering an advancement toward safer and more intelligent high-speed railway operations.