SDIGLM: Leveraging Large Language Models and Multi-Modal Chain of Thought for Structural Damage Identification

• URL: http://arxiv.org/abs/2504.11477v1
• Date: Sat, 12 Apr 2025 11:37:10 GMT
• Title: SDIGLM: Leveraging Large Language Models and Multi-Modal Chain of Thought for Structural Damage Identification
• Authors: Yunkai Zhang, Shiyin Wei, Yong Huang, Yawu Su, Shanshan Lu, Hui Li,
• Abstract summary: This study introduces SDIGLM, an innovative multi-modal structural damage identification model.<n>By leveraging this multi-modal CoT, SDIGLM surpasses general-purpose LMMs in structural damage identification, achieving an accuracy of 95.24% across various infrastructure types.
• Score: 2.9239817922453333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing computer vision(CV)-based structural damage identification models demonstrate notable accuracy in categorizing and localizing damage. However, these models present several critical limitations that hinder their practical application in civil engineering(CE). Primarily, their ability to recognize damage types remains constrained, preventing comprehensive analysis of the highly varied and complex conditions encountered in real-world CE structures. Second, these models lack linguistic capabilities, rendering them unable to articulate structural damage characteristics through natural language descriptions. With the continuous advancement of artificial intelligence(AI), large multi-modal models(LMMs) have emerged as a transformative solution, enabling the unified encoding and alignment of textual and visual data. These models can autonomously generate detailed descriptive narratives of structural damage while demonstrating robust generalization across diverse scenarios and tasks. This study introduces SDIGLM, an innovative LMM for structural damage identification, developed based on the open-source VisualGLM-6B architecture. To address the challenge of adapting LMMs to the intricate and varied operating conditions in CE, this work integrates a U-Net-based semantic segmentation module to generate defect segmentation maps as visual Chain of Thought(CoT). Additionally, a multi-round dialogue fine-tuning dataset is constructed to enhance logical reasoning, complemented by a language CoT formed through prompt engineering. By leveraging this multi-modal CoT, SDIGLM surpasses general-purpose LMMs in structural damage identification, achieving an accuracy of 95.24% across various infrastructure types. Moreover, the model effectively describes damage characteristics such as hole size, crack direction, and corrosion severity.

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