Bridge monitoring data analysis and automated report generation using multimodal large language model

Structural health monitoring (SHM) assessment reports are crucial for bridge maintenance and management, but their generation still relies on manual analysis, with full automation not yet achieved. This study introduces an AI-agent-enabled, automated, and intelligent framework for SHM analysis and report generation. A Python-based system was developed to process heterogeneous monitoring data with minimal human intervention, while a domain-specific multimodal large language model (MLLM), fine-tuned through low-rank adaptation, serves as the reasoning component of the AI agent to address the challenge of interpreting unstructured information. Following necessary data standardization and configuration, the framework enables automated analysis, graphical interpretation, and multilevel textual summary generation, thereby bridging the gap between raw monitoring data and engineering decision support. A representative case study on the Mingzhou Bridge was used to verify the engineering feasibility and reporting capability of the proposed framework, while further applications to 10 in-service bridges (5466 GB in total) were conducted to demonstrate its computational efficiency, scalability, and adaptability across heterogeneous SHM systems. Across these applications, the fine-tuned MLLM processed approximately 2500 figures in a total of 308 min and generated approximately 837,000 words on typical personal computer (PC) resources, demonstrating its potential as a scalable and adaptable framework for automated SHM analysis, interpretation, and report generation.