基于云模型的装配式地铁车站施工安全风险评价

doi:10.16265/j.cnki.issn1003-3033.2023.06.0860

摘要/Abstract

摘要：

为有效评价装配式地铁车站施工安全风险,减少施工安全事故发生,保障施工安全,提出基于组合赋权和云模型的风险评价方法。首先,根据国家规范和项目资料,初步识别装配式地铁车站施工安全风险因素;再利用德尔菲法优化风险因素,建立包括6个一级指标和25个二级指标的装配式地铁车站施工安全风险评价指标体系;然后,采用熵权法和组合数有序加权(C-OWA)算子确定指标的组合权重,进而结合云模型确定风险评价等级;最后,基于某装配式地铁车站项目验证上述评价方法。结果表明:该项目评价等级为较低风险,与施工现场实际情况基本一致,但人员和施工技术的风险等级为中等,在采取安全防控措施时应重点考虑。

关键词: 装配式地铁车站, 施工安全, 风险评价, 云模型

Abstract:

In order to effectively evaluate the construction safety risk of prefabricated subway stations, reduce construction safety accidents and ensure construction safety, a risk evaluation model based on a combination weighting and cloud model was proposed. Firstly, the construction safety risk factors were identified based on national standards and project information and then optimized by the Delphi method, to establish a construction safety risk assessment index system including 6 first-level and 25 second-level indexes. Then, using the entropy weight method and continuous ordered weighted averaging (C-OWA) operator to determine the index weight and the cloud model to determine the risk assessment level. Finally, the above evaluation method was verified based on a prefabricated subway station project. The results show that the evaluation grade of the project is relatively low risk, which is consistent with the actual situation of the construction site. However, the risk level of personnel and construction technology is medium, which should be given priority consideration when taking safety prevention and control measures.

Key words: prefabricated subway station, construction safety, risk assessment, cloud model

罗振华, 郭俊涛, 韩建强. 基于云模型的装配式地铁车站施工安全风险评价[J]. 中国安全科学学报, 2023, 33(6): 88-95.

LUO Zhenhua, GUO Juntao, HAN Jianqiang. Construction safety risk assessment of prefabricated subway station based on cloud model[J]. China Safety Science Journal, 2023, 33(6): 88-95.

图/表 11

图1

图2

表1

表2

图3

表3

二级指标云数字特征

二级指标	$E x j$	$E n j$	$H e j$
R₁₁	70.832 0	3.865 6	0.502 1
R₁₂	69.545 2	2.764 8	1.638 5
R₁₃	70.872 1	4.237 2	2.752 4
R₂₁	84.367 8	4.852 4	1.683 7
R₂₂	78.569 5	5.533 7	2.545 8
R₂₃	82.274 4	4.468 2	1.287 1
R₃₁	83.189 5	4.623 1	0.926 1
R₃₂	77.870 6	3.763 2	1.511 7
R₃₃	79.623 8	5.034 6	0.493 4
R₃₄	78.485 4	3.782 5	1.357 3
R₃₅	80.358 6	4.368 3	0.497 2
R₄₁	66.592 6	4.056 2	1.249 3
R₄₂	68.269 1	5.670 6	1.104 1
R₄₃	70.257 6	4.645 8	1.362 4
R₄₄	67.468 5	4.260 5	1.137 6
R₄₅	73.638 2	3.357 3	1.093 4
R₄₆	66.377 8	5.236 9	1.742 5
R₄₇	75.896 5	3.567 3	0.890 1
R₅₁	86.150 1	5.332 5	0.783 2
R₅₂	87.065 5	4.949 6	0.347 5
R₅₃	82.438 4	5.463 1	0.564 7
R₆₁	80.764 2	4.538 8	1.674 6
R₆₂	86.235 4	7.221 5	2.062 4
R₆₃	76.124 7	5.364 1	1.316 2
R₆₄	84.224 3	5.902 7	1.067 6

表3

表4

图4

表5

表6

表7

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指标	客观权重	主观权重	组合权重
R₁	0.184 0	0.171 4	0.178 3
R₂	0.119 9	0.133 2	0.125 9
R₃	0.192 4	0.191 9	0.192 2
R₄	0.224 9	0.201 4	0.214 3
R₅	0.147 8	0.145 5	0.146 7
R₆	0.131 0	0.156 7	0.142 6
R₁₁	0.358 1	0.337 5	0.352 3
R₁₂	0.385 5	0.348 0	0.374 9
R₁₃	0.256 3	0.314 6	0.272 8
R₂₁	0.285 7	0.296 6	0.289 6
R₂₂	0.388 9	0.360 2	0.378 8
R₂₃	0.325 3	0.343 3	0.331 7
R₃₁	0.214 2	0.224 7	0.218 8
R₃₂	0.197 5	0.195 2	0.196 5
R₃₃	0.224 1	0.210 9	0.218 4
R₃₄	0.171 3	0.193 4	0.180 9
R₃₅	0.192 9	0.175 7	0.185 5
R₄₁	0.212 3	0.162 1	0.192 2
R₄₂	0.149 2	0.148 6	0.149 0
R₄₃	0.169 7	0.160 5	0.166 0
R₄₄	0.143 7	0.146 8	0.144 9
R₄₅	0.111 4	0.133 5	0.120 3
R₄₆	0.100 2	0.123 0	0.109 3
R₄₇	0.113 6	0.125 4	0.118 3
R₅₁	0.302 1	0.325 5	0.310 7
R₅₂	0.325 5	0.309 5	0.319 6
R₅₃	0.372 4	0.365 0	0.369 7
R₆₁	0.237 8	0.224 5	0.232 7
R₆₂	0.333 1	0.272 6	0.309 9
R₆₃	0.217 2	0.259 8	0.233 6
R₆₄	0.211 9	0.243 0	0.223 9

风险等级	评分区间	数字特征(E_x,E_n,H_e)
低风险	[90,100]	(95,1.67,0.5)
较低风险	[75,90)	(82.5,2.5,0.5)
中风险	[50,75)	(62.5,2.5,0.5)
较高风险	[25,50)	(37.5,4.17,0.5)
高风险	[0,25)	(12.5,4.17,0.5)

一级指标	E_x	E_n	H_e
R₁	70.360 5	3.609 4	1.542 0
R₂	81.477 2	5.003 8	1.878 7
R₃	79.989 7	4.372 8	0.945 1
R₄	69.502 5	4.475 0	1.222 9
R₅	85.070 5	5.262 9	0.563 2
R₆	82.150 4	5.952 6	1.575 2

相似度	低风险	较低风险	中风险	较高风险	高风险
R	0.000 2	0.265 9	0.036 7	0.017 6	0.000 9
R₁	0.000 0	0.070 3	0.171 8	0.043 8	0.001 6
R₂	0.001 7	0.370 7	0.011 9	0.015 7	0.000 3
R₃	0.001 0	0.338 8	0.016 5	0.012 9	0.000 0
R₄	0.000 0	0.058 7	0.204 8	0.043 8	0.000 9
R₅	0.013 0	0.336 5	0.003 7	0.008 9	0.000 3
R₆	0.001 3	0.365 6	0.008 5	0.010 5	0.000 3

指标	文中方法	熵权-云模型	模糊综合评判	灰色聚类分析
R	77.266 4	77.121 8	75.410 4	79.458 7
R₁	70.360 5	70.335 6	71.286 3	72.025 8
R₂	81.477 4	81.527 1	79.080 3	81.121 6
R₃	79.989 7	79.992 4	77.666 9	80.850 4
R₄	69.502 5	69.404 9	69.128 8	71.611 5
R₅	85.070 5	85.197 1	77.657 4	81.882 6
R₆	82.150 4	82.200 3	81.075 0	81.230 9