A Large Language Model for Disaster Structural Reconnaissance Summarization

TL;DR

This paper introduces a novel LLM-based framework for disaster structural reconnaissance that integrates vision data and metadata to generate comprehensive summary reports, enhancing post-disaster assessment efficiency.

Contribution

It presents a new LLM-driven approach for integrating vision-based SHM data with structured metadata to produce automated reconnaissance summaries.

Findings

LLM-DRS effectively generates detailed structural damage reports.

The framework improves speed and accuracy of post-disaster assessments.

Promising potential for enhancing resilience through rapid reconnaissance.

Abstract

Artificial Intelligence (AI)-aided vision-based Structural Health Monitoring (SHM) has emerged as an effective approach for monitoring and assessing structural condition by analyzing image and video data. By integrating Computer Vision (CV) and Deep Learning (DL), vision-based SHM can automatically identify and localize visual patterns associated with structural damage. However, previous works typically generate only discrete outputs, such as damage class labels and damage region coordinates, requiring engineers to further reorganize and analyze these results for evaluation and decision-making. In late 2022, Large Language Models (LLMs) became popular across multiple fields, providing new insights into AI-aided vision-based SHM. In this study, a novel LLM-based Disaster Reconnaissance Summarization (LLM-DRS) framework is proposed. It introduces a standard reconnaissance plan in which the collection of vision data and corresponding metadata follows a well-designed on-site investigation process. Text-based metadata and image-based vision data are then processed and integrated into a unified format, where well-trained Deep Convolutional Neural Networks extract key attributes, including damage state, material type, and damage level. Finally, all data are fed into an LLM with carefully designed prompts, enabling the LLM-DRS to generate summary reports for individual structures or affected regions based on aggregated attributes and metadata. Results show that integrating LLMs into vision-based SHM, particularly for rapid post-disaster reconnaissance, demonstrates promising potential for improving resilience of the built environment through effective reconnaissance.