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Intelligent question answering model for construction safety hazards based on vision-language multimodality
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Zhe WANG1, 2, Haichen HUANG1, 2, Ruiqin LI1, 2, Yongchang WEI3
China Safety Science Journal | 2025, 35(10) : 106 - 114
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China Safety Science Journal | 2025, 35(10): 106-114
Safety engineering technology
Intelligent question answering model for construction safety hazards based on vision-language multimodality
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Zhe WANG1, 2, Haichen HUANG1, 2, Ruiqin LI1, 2, Yongchang WEI3
Affiliations
  • 1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
  • 2China Emergency Management Research Center, Wuhan University of Technology, Wuhan Hubei 430070, China
  • 3College of Business Administration, Zhongnan University of Economics and Law, Wuhan Hubei 430073, China
Published: 2025-10-28 doi: 10.16265/j.cnki.issn1003-3033.2025.10.1435
Outline
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In order to enhance the intelligent diagnosis level of safety problems in complex construction environments, an intelligent question-answering model for construction safety hazards based on vision-language multimodality was proposed. A dataset of image-text pairs related to construction safety hazards was constructed. A visual encoder was used to complete the visual encoding of safety hazard images, and a language model was employed to encode the question-answering texts about safety hazards. A multimodal feature fusion module was adopted to achieve effective interaction between image and text information. A specific input template for visual question answering adapted to the scenario of construction safety hazards was constructed. The model was fine-tuned based on matrix low-rank decomposition, and multi-round prompts were used to guide the model in generating accurate answers. The results show that compared with existing contrastive models, the intelligent question-answering model for construction safety hazards performs better in automatic evaluation metrics, Generative Pre-trained Transformer(GPT)-4 evaluation, and expert evaluation, with significantly improved fluency and semantic relevance of the generated texts. Ablation experiments further verify the effectiveness of each sub-module, confirming that the synergistic effect of matrix low-rank decomposition fine-tuning and multi-round reasoning is the key for the model to achieve optimal performance, and that reasonably setting the rank parameter of the low-rank matrix can effectively avoid the overfitting problem.

vision-language  /  multimodality  /  construction safety  /  safety hazard  /  intelligent question answering model  /  matrix low-rank decomposition
Zhe WANG, Haichen HUANG, Ruiqin LI, Yongchang WEI. Intelligent question answering model for construction safety hazards based on vision-language multimodality[J]. China Safety Science Journal, 2025 , 35 (10) : 106 -114 . DOI: 10.16265/j.cnki.issn1003-3033.2025.10.1435
Year 2025 volume 35 Issue 10
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.10.1435
  • Receive Date:2025-06-10
  • Online Date:2026-07-09
  • Published:2025-10-28
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  • Received:2025-06-10
  • Revised:2025-08-13
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Affiliations
    1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
    2China Emergency Management Research Center, Wuhan University of Technology, Wuhan Hubei 430070, China
    3College of Business Administration, Zhongnan University of Economics and Law, Wuhan Hubei 430073, China
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