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arXiv · 2508.02073

Large model retrieval enhancement framework for construction site risk identification

Jiawei Li·Chengye Yang·Yaochen Zhang·Weilin Sun·Lei Meng·Xiangxu Meng
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Abstract

This study addresses construction site hazard identification by proposing a retrieval-augmented framework that enhances large language models (LLMs) without requiring fine-tuning. Current LLM-based approaches face limitations: image-text matching struggles with complex hazards, while instruction tuning lacks generalization and is resource-intensive. Our method dynamically integrates external knowledge and retrieved similar cases via prompt tuning, overcoming LLMs' limitations in domain knowledge and feature correlation. The framework comprises a case database, an image retrieval module, and an LLM-based reasoning module. Evaluated on real-site data, our approach boosted GLM-4V's accuracy to 50%, a 35.49% improvement over baselines, with consistent gains across hazard types. Ablation studies validated the effectiveness of our image retrieval strategy, showing the superiority of our LPIPS- and CLIP-based method. The proposed technique significantly improves identification accuracy and contextual understanding, demonstrating strong generalization and offering a practical path for intelligent safety risk detection in construction.

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Jiawei Li, Chengye Yang, Yaochen Zhang, Weilin Sun, Lei Meng, Xiangxu Meng. 2025-08-04. Large model retrieval enhancement framework for construction site risk identification. https://arxiv.org/abs/2508.02073

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