Evidence Constrained Agentic Retrieval Augmented Generation for Substation Civil Engineering Preliminary Design Documents in a Single Project Study
Abstract
Abstract Substation civil engineering preliminary design documents must integrate project-specific conditions, current regulatory requirements, typical design practices, and historical engineering experience while maintaining consistency across architectural, structural, general-layout, foundation, road, drainage, and technical-indicator sections. Conventional Retrieval-Augmented Generation (RAG) systems remain vulnerable to condition-insensitive retrieval, unequal source authority, incompatible evidence, unsupported engineering claims, and parameter drift during long-document drafting. This study proposes an Evidence-Constrained Agentic Retrieval-Augmented Generation framework, termed EC-ARAG. The framework combines regulation-aware evidence representation, validity-gated hybrid retrieval, source-role-aware conflict resolution, dual planning--retrieval and generation--verification--repair loops, and global project-state memory. The experimental corpus comprised 36 national and industry standards, 12 enterprise standards, 18 typical-design documents, 64 historical preliminary design documents, and nine project-input documents. The held-out evaluation included 120 retrieval queries, 80 applicability cases, 48 evidence-conflict cases, 318 atomic design propositions, and five independent generation runs. EC-ARAG achieved a Recall@5 of 94.1\%, an applicability Macro-F1 of 90.6\%, an evidence support rate of 95.0\%, section completeness of 96.0\%, and cross-section parameter…
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