TunnelSentinel: An Agentic AI Framework for Geo-Structural Resilience and Settlement Safety in Immersed Tunnels

Managing settlement risks during the Operation and Maintenance (O&M) phase of immersed tunnels is critical for preventing structural hazards, particularly in mega-infrastructures like the Hong Kong-Zhuhai-Macau Bridge (HZMB). However, conventional risk management relies heavily on fragmented data across heterogeneous sources, manual calculations, and implicit expert knowledge. These dependencies create significant inefficiencies and susceptibility to human error, potentially compromising disaster prevention efforts. To address these challenges, this study introduces TunnelSentinel, a novel Multi-Agent System (MAS) powered by Large Language Models (LLMs) capable of executing end-to-end settlement management processes. The framework integrates three core innovations: (1) a robust multi-agent architecture (comprising Orchestrator, Retriever, Simulator, and Reporter agents) that automates collaboration for complex decision-making while ensuring process transparency; (2) a Structure-Guided Retrieval-Augmented Generation (SG-RAG) method designed to accurately extract insights from hierarchical engineering and geological project documents; and (3) an optimized model configuration strategy balancing performance with computational efficiency. Applied to the HZMB, TunnelSentinel reduced average task completion time to under 62 seconds—a 126× speed improvement over manual operations—while maintaining accuracy exceeding 97% in information retrieval, settlement calculation, and scenario planning. This work demonstrates the transformative potential of Agentic AI in geosciences, offering a scalable solution for autonomous infrastructure resilience and safety.