China Safety Science Journal ›› 2022, Vol. 32 ›› Issue (1): 27-33.doi: 10.16265/j.cnki.issn1003-3033.2022.01.004

• Safety social science and safety management • Previous Articles     Next Articles

An early warning model of unsafe behaviors of construction workers based on BP neural network

SHI Juan1(), CHANG Dingyi1, ZHENG Peng1,2,**()   

  1. 1School of Management, Tianjin University of Technology, Tianjin 300384, China
    2Huadian Electric Power Research Institute Co., Ltd., Hangzhou Zhejiang 310030, China
  • Received:2021-10-08 Revised:2021-12-10 Online:2022-01-28 Published:2022-07-28
  • Contact: ZHENG Peng

Abstract:

In order to reduce unsafe behaviors of construction workers and improve safety management of corporations, methods of statistical analysis, literature analysis and qualitative interview were adopted to obtain influencing factors of unsafe behaviors. Then, an early warning index system was established from four aspects, namely organization, individual, environment and equipment. Based on BP neural network principle, with these indicators as network input, and four unsafe states were output, a warning questionnaire was designed, and the questionnaire data were repeatedly trained and learned. Finally, a three-layer BP neural network warning model of "23-9-4" was constructed, trained and tested. The results show that this model accurately predicts unsafe behavior states of workers, thereby enabling them to take prevention and control measures in advance.

Key words: back propagation (BP) neural network, construction workers, unsafe behavior, pre-warning model, index system