|本期目录/Table of Contents|

[1]李书全,吴秀宇,袁小妹,等.基于GA-SVM的施工人员安全行为影响因素及决策模型研究[J].中国安全生产科学技术,2014,10(12):185-191.[doi:10.11731/j.issn.1673-193x.2014.12.031]
 LI Shu-quan,WU Xiu-yu,YUAN Xiao-mei,et al.Study on influencing factors and decision model of safety behaviors for construction personnel based on GA-SVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2014,10(12):185-191.[doi:10.11731/j.issn.1673-193x.2014.12.031]
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基于GA-SVM的施工人员安全行为影响因素及决策模型研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
10
期数:
2014年12期
页码:
185-191
栏目:
职业安全卫生管理与技术
出版日期:
2014-12-31

文章信息/Info

Title:
Study on influencing factors and decision model of safety behaviors for construction personnel based on GA-SVM
作者:
李书全1 吴秀宇1袁小妹1周远2
(1天津财经大学 商学院,天津 300222;2天津财经大学 经济学院,天津 300222)
Author(s):
LI Shu-quan1 WU Xiu-yu1 YUAN Xiao-mei1 ZHOU Yuan2
(1.School of Business, Tianjin University of Finance &Economics, Tianjin 300222, China; 2.School of Economics, Tianjin University of Finance &Economics, Tianjin 300222, China)
关键词:
安全行为影响因素决策模型遗传算法(GA)支持向量机(SVM)
Keywords:
safety behaviors influencing factor decision model genetic algorithm (GA) support vector machine (SVM)
分类号:
X947
DOI:
10.11731/j.issn.1673-193x.2014.12.031
文献标志码:
A
摘要:
导致施工人员不安全行为的因素众多,如何保证员工进行安全施工是施工企业亟待解决的问题。为分析施工人员安全行为的影响因素及作用机理,从社会资本理论、认知心理学理论和安全行为理论分析了安全行为的影响因素,并设计了相关调查问卷。在进行仿真分析前,采用遗传算法优化计算的方法筛选出了13个关键影响因素,降低了自变量之间的相关性,之后利用支持向量机(SVM)的方法对决策模型进行了仿真分析,并与BP神经网络模型做了对比。仿真结果表明:基于筛选出的关键影响因素的SVM仿真模型的精度和有效性大于BP神经网络模型,模型精度为000887,相关系数为884%,说明影响因素与安全行为之间具有较好的拟合关系。研究结论为企业衡量员工安全行为水平,提高员工安全行为能力和企业安全管理能力提供理论支持。
Abstract:
There are many factors leading to the unsafe behaviors of construction personnel, and how to ensure the staff to conduct safety construction is an urgent problem in construction enterprises. To analysis the influencing factors and action mechanism of safety behaviors for construction personnel, combining with the theories of social capital, cognitive psychology and safety behavior, the influencing factors of safety behaviors were analyzed, and the corresponding questionnaire was designed. Before the simulation, the genetic algorithm was applied to screen out 13 key influencing factors, and the correlation between independent variables was reduced. The simulation analysis on decision model was conducted by SVM, and a comparison with BP neural network model was presented. The simulation results showed that based on the selected key influencing factors, the accuracy of the SVM model was greater than that of BP neural network, the model precision was 0.00887, and the correlation coefficient was 88.4%, and there had a better fitting relationship between influencing factors and safety behaviors. The results can provide theoretical support for enterprise to measure the safety behaviors level of employee, and improve the safety behaviors ability of employee and safety management ability of enterprise.

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备注/Memo

备注/Memo:
国家自然科学基金项目(71171140);天津市科委项目(12JCZDJC34900);天津财经大学重点科研项目(ZD1306);天津财经大学研究生科研资助计划项目(2014TCB05)
更新日期/Last Update: 2014-12-30