基于边缘智能体RAG的FHWA桥梁检查合规自动化系统
原标题：Edge-Based Agentic Retrieval-Augmented Generation for Autonomous FHWA Bridge Inspection Compliance
AI 摘要
本文介绍了 BridgeGuard，一个完全离线（air-gapped）的基于智能体检索增强生成（RAG）的系统，用于自动化 FHWA 桥梁检查合规性验证。该系统在边缘硬件上本地运行，结合向量搜索和 SQL 查询，通过多步骤 ReAct 循环实现，在 Delaware 和 Texas 的桥梁数据上分别达到 99.77% 和 100% 的分类准确率，并实现 100% 的引用准确率。该系统解决了无网络环境下的合规检查问题，提高了效率并减少了人工错误。
正文节选
Edge-Based Agentic Retrieval-Augmented Generation for Autonomous FHWA Bridge Inspection Compliance Abstract The Federal Highway Administration (FHWA) mandates that over 600,000 bridges in the United States be evaluated against the Recording and Coding Guide for the National Bridge Inventory (NBI). Manual compliance verification is labor-intensive, error-prone, and impractical in connectivity-limited field environments. This paper introduces BridgeGuard, a fully air-gapped agentic Retrieval-Augme