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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

Arabic

وتتطلب تقييم البنية الأساسية من التقييم المستقبلي للبنية التحتية تقييم التقييم البنية الأساسية لتقييم البنية الأساسية لتأثيرها على السلامة والمستوى المعيشي والتنمية. ومع ذلك، فإن الأدلة بعد الحوادث غالباً ما تكون متناقضة، غير كافية، وتتوزع على الصور والرسائل التقييمية، في حين أن النماذج التقييمية تتطلب عادة إدخال إشارات التقييم البنية الأساسية المختلطة. يتم تحويل هذه الدراسة إلى نموذجًا متكاملًا لتقييم البنية الأساسية لتقييم البنية الأساسية لتقييم البنية الأساسية لتقييم البنية الأساسية وتقييم البنية الأساسية لتقييم البنية

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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