SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring

2026-05-12

AI Summary

This study addresses the limitations of conventional structural health monitoring (SHM) approaches—such as complex deployment, poor interoperability, and cumbersome training procedures—by proposing a novel hybrid agent framework that integrates the reasoning capabilities of large language models (LLMs) with specialized SHM algorithms. For the first time, this framework enables natural language–driven, end-to-end execution of SHM tasks. Built upon a modular architecture, the system seamlessly combines LLMs, deep learning pretraining, and multiple domain-specific algorithms, substantially enhancing scalability and ease of deployment. Experimental validation on a long-span cable-stayed bridge demonstrates the system’s effectiveness in accurately and efficiently performing over ten distinct SHM tasks, including anomaly diagnosis, modal identification, damage detection, and fatigue assessment, thereby confirming its robust generalizability and practical utility.

Abstract

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.

Problem

• structural health monitoring
• implementation barriers
• interoperability
• complex training procedures

Innovation

• generalist-specialist agent
• structural health monitoring
• large language model
• modular design
• end-to-end SHM