跨层次视角下建筑工人安全行为预警

Early Warning of Construction Workers’ Safety Behavior from Cross-level Perspective

以行为安全理论为基础,根据文献分析和人员访谈,基于跨层次的系统视角构建了建筑工人安全行为预警体系.根据该体系,制作了调查问卷,面向现场建筑工人进行了数据收集,并采用反向传播(BP)神经网络对该体系进行了训练和测试.结果表明:仿真得出的输出值和实际安全行为数据一致性较高,训练后的神经网络模型仿真能力较强,该评价体系能有效地对建筑工人的安全行为进行评价和预警.根据阈值,把安全行为分为优秀、良好、较差等3个等级,并针对预警结果采取不同的安全干预方案,从而提高安全管理能力,改进项目安全绩效.

Based on the theory of behavior safety, the literature analysis and personnel interviews, the early warning system for the safety behavior of construction workers was established from a cross-level systematic perspective. According to the system, the questionnaire was designed, the data were collected from construction sites, and the system was trained and tested by back propagation(BP) neural network. The results show that output values of the simulation accord well with the actual safety behavior data, and the simulation ability of the trained neural network model is competitive. The evaluation system could effectively evaluate and predict the safety behavior of the construction workers. At the same time, safety behavior was sorted into three grades(excellent, good and poor) according to the threshold, and different safety intervention programs should be adopted to enhance the safety management ability and improve the projects’ safety performance.