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A Multimodal Engineering GraphRAG Framework for Post-Wind-Event Building Assessment and Recovery Inference

Seyedeh Fatemeh Mirfakhar, Reda Snaiki, Rocio L. Segura · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28216 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

リサイクル・コンテキスト・アウトプローバーの基本的な評価は、観測されたフレームの物理的損傷を安全性、居住性、機能性、および回復への影響に結びつけることを必要とします。しかし、リサイクル・コンテキスト・アウトプローバーの証拠は、しばしば異質で、不完全で、画像や認識レポートに分散され、回復モデルは通常構造化されたエンジニアリング・インプットを必要とします。この研究は、リサイクル・コンテキスト・エンジニアリング・グラフ・アウトプローバー(GraphRAG)フレームワークを開発し、リサイクル・エンジニアリング・グラフ・アウトプローバー(GraphR

Machine translation (Meta M2M100); the English original follows.

Abstract

Post-event building assessment requires linking observed physical damage to its consequences for safety, habitability, functionality, and recovery. Yet post-disaster evidence is often heterogeneous, incomplete, and distributed across images and reconnaissance reports, while recovery models typically require structured engineering inputs. This study develops a multimodal engineering Graph Retrieval-Augmented Generation (GraphRAG) framework that connects post-wind-event damage evidence to physical consequences, building-condition states, recovery actions, and downtime. Image- and text-based evidence is transformed into a provenance-preserving, component-level representation distinguishing confirmed damage, explicit negative evidence, unresolved conditions, and temporary measures. Graph-aligned retrieval combines comparable building scenarios with explicit engineering relationships, while compatibility, contradiction, evidence-precedence, and abstention mechanisms constrain inference under partial observability. Recovery actions are derived from supported damage-consequence relationships, and downtime uncertainty is propagated through an action-dependency network using Monte Carlo simulation. The framework is demonstrated for tornado-affected low-rise residential buildings using reconnaissance and image-based evidence. Comparisons with BaselineRAG and Structured Baseline RAG show improved agreement for key building-condition and recovery outcomes, together with traceable engineering reasoning and preserved evidence provenance. Ablation analyses demonstrate the contributions of engineering context, evidence safeguards, and graph-path traversal. The results support transparent, recovery-oriented assessment from incomplete multimodal post-event evidence.

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Topics: Downtime, Inference, Computer science, Representation (politics), Baseline (sea), Artificial intelligence

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