基于RF-SFLA-SVM的装配式建筑高空作业工人不安全行为预警
Research on early warning for prefabricated building workers'unsafe behaviors of working at height based on RF-SFLA-SVM
王军武 1何娟娟 2宋盈辉 2刘一鹏 1陈兆 2郭婧怡3
作者信息
- 1. 武汉理工大学三亚科教创新园,海南三亚 572025;武汉理工大学土木工程与建筑学院,湖北武汉 430070
- 2. 武汉理工大学土木工程与建筑学院,湖北武汉 430070
- 3. 湖北文理学院土木工程与建筑学院,湖北襄阳 441053
- 折叠
摘要
为有效预警装配式建筑高空作业工人不安全行为的发生趋势或状态,增强对装配式建筑工人不安全行为(PBWUBs)的管控,采用随机森林(RF)-混合蛙跳算法(SFLA)-支持向量机(SVM)模型,开展工人不安全行为预警研究.首先,采用SHEL模型分析处于高空作业危险中的PBWUBs的影响因素,并通过RF确定关键预警指标;然后,采用SFLA对SVM的参数进行寻优改进;最后,利用RF-SFLA-SVM预警高空作业PBWUBs,提出应对措施,并与其他预警模型对比.研究结果表明:基于RF-SFLA-SVM预警高空作业PBWUBs,准确率最高,为91.67％,与其他模型的预警性能相比,最高提升14％.研究结果可为高空作业PBWUBs的防控提供参考.
Abstract
In order to effectively provide early warning of the occurrence trend or state of prefabricated building workers'unsafe behaviors(PBWUBs)of working at height,and to enhance the control of PBWUBs,RF-SFLA-SVM model was proposed to conduct an early warning study on workers'unsafe behaviors.Firstly,the SHEL(Software-Hardware-Environment-Liveware)model was used to analyze the factors influencing the unsafe behaviors of prefabricated building workers in danger of working at height.RF was used to determine the key warning indicators.Then SFLA was used to find the best parameters for SVM.Finally,the RF-SFLA-SVM model was used to predict and warn about the unsafe behavioral state of the prefabricated building workers working at height,and its performance was compared with other warning models.The results show that the RF-SFLA-SVM-based warning accuracy of PBWUBs of working at height was the highest,91.67％,which was a maximum improvement of 14％compared with the warning performance of other models.The research results can give a reference for the control and prevention of PBWUBs working at height.
关键词
随机森林(RF)/蛙跳算法(SFLA)/支持向量机(SVM)/装配式建筑/高空作业/不安全行为Key words
random forest(RF)/shuffled frog leaping algorithm(SFLA)/support vector machine(SVM)/prefabricated buildings/working at height/unsafe behaviors引用本文复制引用
基金项目
海南省重大科技计划(2021)(ZDKJ2021024)
三亚崖州湾科技城科技专项(SCKJ-JYRC-2022-81)
三亚科教创新园开发基金(2022KF0003)
出版年
2024
NETL
NSTL
万方数据