人-管-环因素耦合下建筑施工事故风险倾向判别

doi:10.16265/j.cnki.issn1003-3033.2023.S1.1485

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (S1): 69-75.doi: 10.16265/j.cnki.issn1003-3033.2023.S1.1485

人-管-环因素耦合下建筑施工事故风险倾向判别

熊坚¹(), 彭一鸣¹, 陈瑶², 宋致科³, 蔡晶¹^,^**()

¹ 昆明理工大学交通工程学院, 云南昆明 650500
² 云南省交通科学研究院有限公司, 云南昆明 650011
³ 中信建设有限责任公司, 北京 100027

收稿日期:2023-02-14 修回日期:2023-04-08 出版日期:2023-06-30
通讯作者:
**蔡晶(1989—),女,云南个旧人,博士,讲师,主要从事交通系统分析等相关教学与科研工作。E-mail:caijing@kust.edu.cn。
作者简介:
熊坚 (1959—),男,云南昭通人,博士,教授,主要从事人机环仿真系统开发应用、驾驶员行为特征、车辆智能辅助系统等方面的研究。E-mail:xiebox@163.com。
陈瑶高级工程师
宋致科高级工程师
基金资助:
云南省交通厅科技创新及示范项目(HZ2021X0302A)

Risk propensity discrimination of construction accidents under coupling of human-management-environment factors

XIONG Jian¹(), PENG Yiming¹, CHEN Yao², SONG Zhike³, CAI Jing¹^,^**()

¹ Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming Yunnan 650500, China
² Yunnan Transportation Research Institute Co., Ltd., Kunming Yunnan 650011, China
³ CITIC Construction Co., Ltd., Beijing 100027, China

Received:2023-02-14 Revised:2023-04-08 Published:2023-06-30

摘要/Abstract

摘要：

为预防和减少建筑施工事故,从人员、管理、外在环境等角度,利用机器学习算法辨识事故发生的风险倾向。首先,针对人、管、环3因素,分别设计并检验施工人员过度自信、组织管理氛围以及施工环境危险水平测量量表;其次,根据量表得分,采用K-means算法划分出不同事故风险倾向类型,提出综合风险因子判断聚类结果;最后,运用主成分分析(PCA)、Borderline-SMOTE以及LightGBM算法逐步构建施工事故风险倾向判别模型,采用k折交叉验证法评估模型稳定性。结果表明:模型预测准确率达90.83%,能够有效辨识建筑事故的风险水平,并将事故风险分为低风险、中等风险、高风险3种倾向类型。

关键词: 建筑施工事故, 风险倾向, 过度自信, 组织管理氛围, 施工环境, LightGBM算法

Abstract:

In order to prevent and reduce construction accidents, a machine learning algorithm was used to identify the risk propensity of accidents from the perspectives of humans, management, and external environments. Firstly, according to the three factors of humans, management, and environments, the scales for measuring the overconfidence of construction personnel, organizational management atmosphere, and construction environment risk levels were designed and tested respectively. Secondly, according to the score of the scale, the K-means algorithm was used to classify different types of accident risk propensity, and comprehensive risk factors were proposed to judge the clustering results. Finally, principal component analysis, Borderline-Smote, and LightGBM algorithms were used to establish the risk propensity discrimination model of construction accidents, and the stability of the model was evaluated by the k-fold cross-validation method. The results show that the prediction accuracy rate of the model is 90.83%. Therefore, the model can effectively identify the risk level of construction accidents, and the accident risks can be divided into three types: low-risk propensity, medium-risk propensity, and high-risk propensity.

Key words: construction accidents, risk propensity, overconfidence, organizational management atmosphere, construction environment, LightGBM algorithm

熊坚, 彭一鸣, 陈瑶, 宋致科, 蔡晶. 人-管-环因素耦合下建筑施工事故风险倾向判别[J]. 中国安全科学学报, 2023, 33(S1): 69-75.

XIONG Jian, PENG Yiming, CHEN Yao, SONG Zhike, CAI Jing. Risk propensity discrimination of construction accidents under coupling of human-management-environment factors[J]. China Safety Science Journal, 2023, 33(S1): 69-75.

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参考文献 21

[1]	国家统计局. 中国统计年鉴[M]. 北京: 中国统计出版社, 2021: 267-268.
[2]	董利飞, 陈飞宇, 王苗, 等. 建筑施工安全事故死亡人数预测与影响因素分析[J]. 安全与环境学报, 2022, 22(3): 1 430-1 435.
	DONG Lifei, CHEN Feiyu, WANG Miao, et al. Analysis on the forecast of construction death and its influence factors[J]. Journal of Safety and Environment, 2022, 22(3): 1 430-1 435.
[3]	ZHANG Fan, FLEYEH H, WANG Xinru, et al. Construction site accident analysis using text mining and natural language processing techniques[J]. Automation in Construction, 2019, 99: 238-248. doi: 10.1016/j.autcon.2018.12.016
[4]	AYHAN B U, TOKDEMIR O B. Accident analysis for construction safety using latent class clustering and artificial neural networks[J]. Journal of Construction Engineering and Management, 2020, 146(3): DOI: 10.1061/(ASCE)CO.1943-7862.0001762.
[5]	赵挺生, 庞奇志, 姜雯茜. 基于数据库与支持向量机的施工升降机安全风险预测[J]. 中国安全科学学报, 2021, 31(4): 11-17. doi: 10.16265/j.cnki.issn1003-3033.2021.04.002
	ZHAO Tingsheng, PANG Qizhi, JIANG Wenxi. Safety risk prediction of construction elevator based on database and SVM[J]. China Safety Science Journal, 2021, 31(4): 11-17. doi: 10.16265/j.cnki.issn1003-3033.2021.04.002
[6]	冯亚娟, 都思竹, 张竞一. 基于EW-SPA的装配式建筑施工安全评价及预测[J]. 中国安全科学学报, 2019, 29(5): 85-90. doi: 10.16265/j.cnki.issn1003-3033.2019.05.015
	FENG Yajuan, DU Sizhu, ZHANG Jingyi. Safety evaluation and prediction of prefabricated building construction based on EW-SPA[J]. China Safety Science Journal, 2019, 29(5): 85-90. doi: 10.16265/j.cnki.issn1003-3033.2019.05.015
[7]	YANG Jingjing, YE Gui, XIANG Qingting, et al. Insights into the mechanism of construction workers' unsafe behaviors from an individual perspective[J]. Safety Science, 2021, 133: DOI: 10.1016/j.ssci.2020.105004.
[8]	TONG J, FEILER D, IVANTSOVA A. Good choice, bad judgment: How choice under uncertainty generates overoptimism[J]. Psychological Science, 2018, 29(2): 254-265. doi: 10.1177/0956797617731637 pmid: 29283751
[9]	COSTA D F, DE MELO CARVALHO F, DE MELO MOREIRA B C, et al. Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias[J]. Scientometrics, 2017, 111(3): 1 775-1 799. doi: 10.1007/s11192-017-2371-5
[10]	KLUSOWSKI J, SMALL D A, SIMMONS J P. Does choice cause an illusion of control?[J]. Psychological Science, 2021, 32(2): 159-172. doi: 10.1177/0956797620958009 pmid: 33400628
[11]	ALKAISSY M, ARASHPOUR M, ASHURI B, et al. Safety management in construction: 20 years of risk modeling[J]. Safety Science, 2020, 129: DOI: 10.1016/j.ssci.2020.104805.
[12]	ZHANG R P, LINGARD H, OSWALD D. Impact of supervisory safety communication on safety climate and behavior in construction workgroups[J]. Journal of Construction Engineering and Management, 2020, 146(8): DOI: 10.1061/(ASCE)CO.1943-7862.0001881.
[13]	AMIRI M, ARDESHIR A, ZARANDI M H F. Fuzzy probabilistic expert system for occupational hazard assessment in construction[J]. Safety Science, 2017, 93: 16-28. doi: 10.1016/j.ssci.2016.11.008
[14]	XIA Nini, ZOU P X W, LIU Xing, et al. A hybrid BN-HFACS model for predicting safety performance in construction projects[J]. Safety Science, 2018, 101: 332-343. doi: 10.1016/j.ssci.2017.09.025
[15]	CRONBACH L J. Coefficient alpha and the internal structure of tests[J]. Psychometrika, 1951, 16(3): 297-334. doi: 10.1007/BF02310555
[16]	TAHERDOOST H, SAHIBUDDIN S, JALALIYOON N. Exploratory factor analysis; concepts and theory[J]. Advances in Applied and Pure Mathematics, 2022, 27: 375-382.
[17]	钟熙, 孙祥娥. 基于Kmeans++聚类的朴素贝叶斯集成方法研究[J]. 计算机科学, 2019, 46(增1): 439-441.
	ZHONG Xi, SUN Xiang'e. Research on naïve bayes ensemble method based on Kmeans++ clustering[J]. Computer Science, 2019, 46(S1): 439-441.
[18]	刘朝阳, 吕永波, 刘步实, 等. 高速公路桥梁施工过程安全评估及预警研究[J]. 北京交通大学学报, 2019, 43(2): 131-136. doi: 10.11860/j.issn.1673-0291.20180102
	LIU Zhaoyang, LYU Yongbo, LIU Bushi, et al. Study on safety evaluation and early warning of highway bridge construction process[J]. Journal of Beijing Jiaotong University, 2019, 43(2): 131-136. doi: 10.11860/j.issn.1673-0291.20180102
[19]	CHOI J, GU B, CHIN S, et al. Machine learning predictive model based on national data for fatal accidents of construction workers[J]. Automation in Construction, 2020, 110: DOI: 10.1016/j.autcon.2019.102974.
[20]	王芳杰, 王福建, 王雨晨, 等. 基于LightGBM算法的公交行程时间预测[J]. 交通运输系统工程与信息, 2019, 19(2): 116-121.
	WANG Fangjie, WANG Fujian, WANG Yuchen, et al. Bus travel time prediction based on light gradient boosting machine algorithm[J]. Journal of Transportation Systems Engineering, 2019, 19(2): 116-121.
[21]	汪学清, 刘爽, 李秋燕, 等. 基于K折交叉验证的SVM隧道围岩分级判别[J]. 矿冶工程, 2021, 41(6): 126-128.
	WANG Xueqing, LIU Shuang, LI Qiuyan, et al. Classification and discrimination of Surrounding rock of tunnel based on SVM of K-fold cross validation[J]. Mining and Metallurgical Engineering, 2021, 41(6): 126-128.

变量	分类	样本数	占比/%
性别	男	268	94.70
性别	女	15	5.30
年龄/a	18~30	54	19.08
	31~40	79	27.92
	41~50	92	32.51
	≥51	58	20.49
文化水平	小学或以下	98	34.63
	初中	142	50.18
	高中或中专	43	15.19
工龄/a	≤1	40	14.13
	1~3	55	19.43
	3~5	49	17.31
	5~10	70	24.73
	10~20	48	16.96
	≥20	21	7.42
作业区域	高空作业	122	43.11
	地面作业	117	41.34
	隧道作业	44	15.55

维度	Cronbach α系数		KMO统计量
过度乐观	0.831	0.738	0.806^***
认知偏差		0.688
控制幻觉		0.669
安全管理	0.714	0.709	0.801^***
安全交流	0.714	0.664	0.801^***
自然环境	0.710	0.645	0.729^***
作业条件	0.710	0.613	0.729^***

变量	事故风险倾向性	CPO	OMC	CERL
事故风险倾向性	1.000	—	—	—
CPO	-0.856^**	1.000	—	—
OMC	-0.532^**	0.442^**	1.000	—
CERL	-0.158^**	0.183^**	0.079	1.000

超参数	搜索范围	结果
max_depth	(1,7,1)	4
num_leaves	(3,16,1)	7
learning_rate	(0.05,0.10,0.01)	0.07
feature_fraction	(0.6,1.0,0.1)	0.7
min_split_gain	(0.0,1.0,0.1)	0.1
reg_alpha	(0.0,1.0,0.1)	0.1
reg_lambda	(0.0,1.0,0.1)	0

真实情况	预测结果
真实情况	低风险倾向	中等风险倾向	高风险倾向
低风险倾向	39	3	0
中等风险倾向	2	25	3
高风险倾向	0	2	35

人-管-环因素耦合下建筑施工事故风险倾向判别

Risk propensity discrimination of construction accidents under coupling of human-management-environment factors

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参考文献 21

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Metrics

本文评价

算法	评价指标
算法	准确率	精确率	召回率	F₁值
LightGBM	90.83	90.19	90.26	90.21
支持向量机	83.49	82.25	81.9	81.63
AdaBoost	73.39	76.76	74.44	73.15

[1]	倪国栋, 方亚琦, 张琦, 丁志华. 风险倾向对新生代建筑工人危险感知的影响机制[J]. 中国安全科学学报, 2023, 33(5): 221-229.
[2]	赵红成, 田秀霞, 杨泽森, 白万荣. 改进YOLOv3的复杂施工环境下安全帽佩戴检测算法[J]. 中国安全科学学报, 2022, 32(5): 194-200.
[3]	张惠玲, 孔德学, 敖谷昌. 基于不同风险倾向的驾驶员路径诱导模型[J]. 中国安全科学学报, 2020, 30(5): 108-114.
[4]	王丹. 基于计划行为理论的矿工违章行为研究[J]. 中国安全科学学报, 2011, 21(4): 7-.