Citation

Shi J, Chang D, Zheng P. China Saf. Sci. J. 2022; 32(1): 27-33.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.01.004

PMID

unavailable

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. © 2022, Editorial Department of China Safety Science Journal. All rights reserved.

Language: zh