Evidence constrained agentic retrieval augmented generation for substation civil engineering preliminary design documents in a single project study

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 consistency of 97.0\%. Its unsafe-claim rate and character-level manual-edit rate were 3.4\% and 7.6\%, respectively. Although agentic verification increased machine inference time, total operational completion time was 21.4\% lower than that of closed-corpus Corrective RAG. Under the evaluated single-project, text-only conditions, EC-ARAG improved the reliability, traceability, and professional editability of regulation-intensive preliminary design drafting.

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Publication Details

Journal
Discover Artificial Intelligence
Published
2026-11-06
DOI
https://doi.org/10.1007/s44163-026-02612-2
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00

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article

Evidence constrained agentic retrieval augmented generation for substation civil engineering preliminary design documents in a single project study

Yizhang Huang, Xinhui Zhang
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Discover Artificial Intelligence
Topic Modeling
article

Evidence constrained agentic retrieval augmented generation for substation civil engineering preliminary design documents in a single project study

Yizhang Huang, Xinhui Zhang
article en

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 consistency of 97.0\%. Its unsafe-claim rate and character-level manual-edit rate were 3.4\% and 7.6\%, respectively. Although agentic verification increased machine inference time, total operational completion time was 21.4\% lower than that of closed-corpus Corrective RAG. Under the evaluated single-project, text-only conditions, EC-ARAG improved the reliability, traceability, and professional editability of regulation-intensive preliminary design drafting.

Discover Artificial IntelligenceVol. 6(1)
Science and Technology Project of State Grid, State Grid Shanghai Municipal Electric Power Company
Industry, innovation and infrastructure
Openalex Percentile: Top 46%
Topic Modeling
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