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.
| Comments: | 19 pages, 20 figures |
| Subjects: | Multiagent Systems (cs.MA); Machine Learning (cs.LG) |
| ACM classes: | I.2.1 |
| Cite as: | arXiv:2605.12916 [cs.MA] |
| (or arXiv:2605.12916v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12916 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuequan Bao [view email]
[v1]
Wed, 13 May 2026 02:44:08 UTC (14,918 KB)