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Knowledge Graph-Based Construction Accidents Detection and Hazard Correction System

In: Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Wanyu Shen

    (Tongji University)

  • Yujie Lu

    (Tongji University
    Tongji University
    Tongji University)

  • Na Wang

    (Tongji University)

Abstract

The domain essential elements, historical data, and other project information of construction safety are scattered and mixed. A terrific amount of dangerous factors affect the safety of construction site personnel. Recently computer vision technology and knowledge management methods have made significant progress in reducing and preventing construction safety accidents. But there is still a lack of effective correction mechanism between construction site information and safety knowledge. This paper established an intelligent hazard correction system of construction safety knowledge based on a knowledge graph (KG). First, we extracted various information on construction safety and established a KG. Then, we can achieve timely feedback of safety-relevant and civilized operation precautions with real-time monitoring of the construction process. Finally, this research uses a case to illustrate the operability of this system. The intelligent correction system we have established includes (1) Construction safety KG; (2) Information recognition and; (3) Correction system. The smart correction system of construction safety knowledge proposed in this paper improves the construction site safety environment and expands the research in construction safety.

Suggested Citation

  • Wanyu Shen & Yujie Lu & Na Wang, 2022. "Knowledge Graph-Based Construction Accidents Detection and Hazard Correction System," Lecture Notes in Operations Research, in: Hongling Guo & Dongping Fang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, pages 817-828, Springer.
    • Handle: RePEc:spr:lnopch:978-981-19-5256-2_64
    DOI: 10.1007/978-981-19-5256-2_64
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