Multi-Modal Multi-Agent System for Bridge Operation and Maintenance based on Large Language Model
Xiangyun Luo, Yunkai Zhang, Yong Huang, Stephen Wu, Hui Li
原文摘要
Bridges are critical transportation infrastructure, and reliable operation and maintenance (O&M) is essential for traffic safety and long-term structural performance. However, bridge O&M faces key bottlenecks: no unified architecture coordinates heterogeneous resources; model inferences lack clear links to physical-mechanical laws; and experts' tacit knowledge remains difficult to formalize. This study proposes a knowledge-enhanced autonomy formalization methodology and apply it to construct the Bridge O&M Multi-Modal Multi-Agent System (BOMMA), which integrates heterogeneous diagnostic actions, physics-mechanics-informed representations, long-term memory, and structured experiential knowledge within a coordinated multi-agent workflow, enabling interpretable and traceable scheduling, analysis, and decision-making. On 3,492 multi-source instructions, the proposed method achieves scheduling recalls of 85.37% for simple and 99.40% for composite tasks, with 95.35% BERT F1-score for O&M action reasoning. Ablation studies confirm the contributions of the components in the autonomy. This study bridges general LLM reasoning and specialized bridge O&M requirements.
cs.MA