Development of an RAG-Integrated Agentic BIM System for Intelligent Railway Maintenance

Modern railway maintenance is transitioning toward a condition-based maintenance system to stably operate the core infrastructure of sustainable smart cities. However, technical limitations remain in manually converting and analyzing massive amounts of inspection data into Building Information Modeling (BIM) objects. This causes information delays and technical severance in data-driven smart-city infrastructure. To address these challenges, this study proposes an Agentic BIM framework that integrates Large Language Model (LLM), Model Context Protocol (MCP), and Retrieval Augmented Generation (RAG) technologies. The proposed methodology standardizes the control channel between the LLM and BIM software through a central MCP server, while securing the accuracy of engineering judgments by utilizing the RAG pipeline to reference national railway-track-maintenance guidelines. System validation results demonstrated that geometric inspection data, including gauge and alignment, were automatically generated as BIM objects without human intervention. Furthermore, the maintenance grades and deadlines for sections exceeding thresholds were immediately highlighted within the model as visual attributes. Consequently, this framework proves an autonomous decision-making system that organically links inspection data with maintenance regulations. By transforming static, manual-labor-centered maintenance workflows into intelligent automated models, it increases the efficiency of railway infrastructure management while providing a scalable technical foundation for overall asset management of future smart-city infrastructure.