摘要
为减少建筑工人不安全行为,提高企业安全管理水平,采用事故统计分析、文献分析、质性访谈方法获取不安全行为影响因素,从组织、个人、外在环境、设备4个方面建立不安全行为预警指标体系,在此基础上,基于反向传播(BP)神经网络原理,将预警指标作为网络输入,不安全行为无警、轻警、中警、重警4种状态作为网络输出,进而设计编制预警问卷,对问卷数据进行反复训练学习,最终构建出“23-9-4”3层结构的BP神经网络预警模型,并对该模型进行训练及测试。结果表明:该预警模型预警能力较强,能够较为准确地预测工人的不安全行为状态,从而可提前采取相应的防控措施。
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.
作者
石娟
常丁懿
郑鹏
SHI Juan;CHANG Dingyi;ZHENG Peng(School of Management,Tianjin University of Technology,Tianjin 300384,China;Huadian Electric Power Research Institute Co.,Ltd.,Hangzhou Zhejiang 310030,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2022年第1期27-33,共7页
China Safety Science Journal
基金
国家自然科学基金资助(71603181)
天津市教委社会科学重大项目(2021JWZD15)
天津市研究生科研创新项目(2021YJSB243)。
