面向美国联邦公路管理局（FHWA）桥梁检查合规性的基于边缘的智能体检索增强生成
Edge-Based Agentic Retrieval-Augmented Generation for Autonomous FHWA Bridge Inspection Compliance
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中文总结 AI 辅助
针对FHWA桥梁合规核查的痛点，提出离线智能体RAG系统BridgeGuard，结合向量搜索与SQL查询，在边缘硬件实现高准确率、高速的结构缺陷桥梁识别。
中文摘要 AI 辅助
美国联邦公路管理局（FHWA）要求对美国超过60万座桥梁，按照《国家桥梁库存（NBI）记录与编码指南》进行评估。手动合规性核查劳动强度大、易出错，且在连接受限的野外环境中不具可行性。本文提出BridgeGuard，一种完全离线的智能体检索增强生成（RAG）系统，用于自主桥梁检查合规性。BridgeGuard将针对FHWA《记录与编码指南》的向量搜索，与针对NBI表格数据的结构化SQL查询相集成，由有状态的多步ReAct规划循环编排，在商用边缘硬件上本地执行。一种感知章节的分块算法保留了层级监管条目边界，分块完整性达94.2%，而朴素固定大小分块仅为28.4%。在完整的2023年特拉华州NBI库存（874座桥梁）和得克萨斯州样本（200座桥梁）上评估，该系统针对结构缺陷桥梁识别的分类准确率分别达99.77%和100.0%，引用准确率为100.0%，在无外部网络访问的情况下处理速度达每小时197.0座桥梁。 ablation实验证实，向量搜索和多步智能体循环均为正确合规推理所必需。
英文摘要
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-Augmented Generation (RAG) system for autonomous bridge inspection compliance. BridgeGuard integrates vector search over the FHWA Recording and Coding Guide with structured SQL queries against NBI tabular data, orchestrated by a stateful multi-step ReAct planning loop executing locally on commodity edge hardware. A section-aware chunking algorithm preserves hierarchical regulatory item boundaries, achieving 94.2% chunk integrity compared with 28.4% for naive fixed-size splitting. Evaluated on the full Delaware 2023 NBI inventory (874 bridges) and a Texas sample (200 bridges), the system achieves 99.77% and 100.0% classification accuracy, respectively, for Structurally Deficient bridge identification, with 100.0% citation accuracy, at 197.0 bridges per hour with out external network access. Ablation experiments confirm that both vector search and the multi-step agentic loop are necessary for correct compliance reasoning.
