An MCP-Based Natural Language Interface for Bridge Digital Twin Construction: Methodology and Initial Validation

Abstract

With an increasing number of bridges entering the maintenance phase, the demand for inspections and structural assessments is steadily growing, while a significant shortage of skilled engineers capable of performing simulation-based predictions poses a critical challenge. To address this issue, this study proposes an automatic bridge structure modeling method based on the Model Context Protocol (MCP). By simply entering geometric information—such as bridge type and span—in natural language, the system can automatically execute the entire workflow, including modeling, mesh generation, and visualization. Specifically, the proposed framework integrates tools such as Gmsh (structural modeling), Blender (geometry generation), and Cesium (visualization) via the MCP, and utilizes a large language model (LLM) for semantic analysis, enabling civil engineers who are not proficient in modeling software to create structural models. System execution confirmed both its usability and scalability, demonstrating its effectiveness for the initial development of digital twins for bridges. Future efforts will focus on improving prompt design, optimizing intermediate processes, and enhancing data integration to support comprehensive life-cycle management of bridge infrastructure.