SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
For structural health monitoring practitioners, this system lowers implementation barriers and simplifies deployment by combining LLM reasoning with specialized algorithms, though it is an incremental integration of existing methods.
SHM-Agents integrates LLMs with specialized algorithms to enable end-to-end execution of diverse structural health monitoring tasks via natural language, achieving accurate performance on a long-span cable-stayed bridge across tasks like anomaly diagnosis, modal identification, and damage identification.
Artificial intelligence is increasingly used to simplify complex tasks. In engineering applications of structural health monitoring (SHM), existing specialized algorithms, while effective, often face high implementation barriers, limited interoperability and complex training procedures. To overcome these challenges, this paper proposes SHM-Agents, a generalist-specialist agent system that integrates the reasoning and planning abilities of large language models with the problem-solving strengths of specialized algorithms. SHM-Agents enables end-to-end execution of single and combined SHM tasks via natural language, supports deep learning pre-training to simplify deployment and allows flexible expansion through a modular design. Experiments on a long-span cable-stayed bridge show that SHM-Agents can accurately and efficiently perform diverse SHM tasks, including data anomaly diagnosis and recovery, signal processing, statistical analysis, modal identification, damage identification, finite element model updating, vehicle load modeling, response calculation, reliability assessment, fatigue estimation and bridge knowledge Q\&A.