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To effectively prevent and control the risk of subway waterlogging accidents,a quantitative analysis method integrating the AcciMap model,FAHP,and triangular fuzzy DEMATEL-ISM method was proposed to investigate the causes of urban subway waterlogging accidents and their coupling associations. Accident causation factors were selected from the system level through AcciMap model. FAHP was used to integrate expert opinions and determine expert weights. The DEMATEL method was used to calculate the importance of accident causes,ISM method was used to reveal the relationship between factors,and a multi-level ladder stepwise graph was constructed. The model was validated by waterlogging accidents on the Zhengzhou subway. The results showed that the core contributing factors leading to the incident were inadequate awareness and prevention measures of major disasters by the management department,the lack of diligence in supervision and inspection duties by regulatory authorities,and ineffective response and handling by operational departments. These factors can be paid special attention to prevent system failures.

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为有效防控城市地铁内涝事故风险,提出一种将事故地图(AcciMap)模型、模糊层次分析法(FAHP)以及三角模糊决策实验室分析法(DEMATEL)-解释结构模型(ISM)方法融合的事故致因系统评价模型,定量挖掘城市地铁内涝事故致因及其耦合作用关系。通过AcciMap模型从系统层面遴选事故致因;用FAHP综合专家意见,确定专家权重;利用DEMATEL方法计算事故致因重要程度,采用ISM方法揭示因素间的相互关系,构造多级阶梯有向图。并以郑州地铁的内涝事故为例,验证提出的方法。结果表明:管理部门对特大灾害认识防范不足、监督部门监察职责履行不认真、运营部门应对处置不力是事故发生的核心致因要素,应予以重点关注,防止该社会技术系统失效。

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邢金朵 (1989—),女,河南宝丰人,博士,讲师,主要从事复杂系统动态风险评估、系统可靠性建模理论与应用等方面的研究。E-mail:

张军,教授

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邢金朵 (1989—),女,河南宝丰人,博士,讲师,主要从事复杂系统动态风险评估、系统可靠性建模理论与应用等方面的研究。E-mail:

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journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=图5, caption=地铁内涝事故致因层次结构模型, figureFileSmall=EIMGLtL4WUe75W4eszQjJA==, figureFileBig=z23l0A7PpkpV7i/+NmpI0Q==, tableContent=null), ArticleFig(id=1167816051543782278, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=EN, label=Fig.6, caption=Causes of subway waterlogging accident cause-effect analysis, figureFileSmall=pWJYuWy31bwH7dGVXiiW7w==, figureFileBig=qgVY1znAojFxHxxCghZQPg==, tableContent=null), ArticleFig(id=1167816051602502537, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=图6, caption=地铁内涝事故致因原因-中心度, figureFileSmall=pWJYuWy31bwH7dGVXiiW7w==, figureFileBig=qgVY1znAojFxHxxCghZQPg==, tableContent=null), ArticleFig(id=1167816051669611404, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=EN, label=Table 1, caption=

Analysis of causal factors of urban subway waterlogging accidents

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 说明
政府 灾害风险意识不强S1
应急行为不当,存在失职渎职S2
城市主要负责人缺乏风险意识;
城市主要负责人对防汛工作缺少分析研判、动员部署等行动
监管机构
与协会
职责履行不认真,监督审查不到位S3 城市应急管理部门未及时向社会发布通知;
城市城乡建设、交通运输部门未有效督促有关单位对城市内涝积水点进行整治;城市管理部门未明确地铁集团运营防汛工作职责
相关企
业管理
设计、建设及验收环节存在问题S4
防灾减灾气象保障水平不足S5
相关企业在五龙口停车场及附近围墙在设计、施工、检验存在违规行为;
气象部门存在气象监测预报能力不足、预警响应联动机制不健全等突出问题
技术和运
营管理
集团层面未编制防汛专项应急预案S6
运营应对处置不当S7
运营线路淹水倒灌隐患排查不到位S8
地铁集团未及时发布线网停运指令,线路淹水隐患排查不到位;
运行控制中心主任调度员行车调度失误;
应急信息报送不规范不及时
事故进程
与人员活
停车场挡水围墙被冲毁S9
涝水灌入地铁隧道S10
指令列车放行、退行S11
列车迫停、乘客被困、疏散中断S12
设备与环境 暴雨S13
地铁口附近挡水、排涝设施存在问题S14
列车所在位置标高较低S15
地铁口处地处低洼S16
地铁车站区间排水设备排洪能力不足S17
), ArticleFig(id=1167816051791246221, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=表1, caption=

地铁内涝事故致因分析

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标 二级指标 说明
政府 灾害风险意识不强S1
应急行为不当,存在失职渎职S2
城市主要负责人缺乏风险意识;
城市主要负责人对防汛工作缺少分析研判、动员部署等行动
监管机构
与协会
职责履行不认真,监督审查不到位S3 城市应急管理部门未及时向社会发布通知;
城市城乡建设、交通运输部门未有效督促有关单位对城市内涝积水点进行整治;城市管理部门未明确地铁集团运营防汛工作职责
相关企
业管理
设计、建设及验收环节存在问题S4
防灾减灾气象保障水平不足S5
相关企业在五龙口停车场及附近围墙在设计、施工、检验存在违规行为;
气象部门存在气象监测预报能力不足、预警响应联动机制不健全等突出问题
技术和运
营管理
集团层面未编制防汛专项应急预案S6
运营应对处置不当S7
运营线路淹水倒灌隐患排查不到位S8
地铁集团未及时发布线网停运指令,线路淹水隐患排查不到位;
运行控制中心主任调度员行车调度失误;
应急信息报送不规范不及时
事故进程
与人员活
停车场挡水围墙被冲毁S9
涝水灌入地铁隧道S10
指令列车放行、退行S11
列车迫停、乘客被困、疏散中断S12
设备与环境 暴雨S13
地铁口附近挡水、排涝设施存在问题S14
列车所在位置标高较低S15
地铁口处地处低洼S16
地铁车站区间排水设备排洪能力不足S17
), ArticleFig(id=1167816051845772174, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=EN, label=Table 2, caption=

Expert scores under different criteria

, figureFileSmall=null, figureFileBig=null, tableContent=
专家得分 专家1 专家2 专家3
工作绩效 8 8 6
工作年限 5 5 3
教育水平 10 10 8
专业 8 8 8
声誉 10 10 8
), ArticleFig(id=1167816051908686736, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=表2, caption=

不同准则下的专家得分

, figureFileSmall=null, figureFileBig=null, tableContent=
专家得分 专家1 专家2 专家3
工作绩效 8 8 6
工作年限 5 5 3
教育水平 10 10 8
专业 8 8 8
声誉 10 10 8
), ArticleFig(id=1167816051971601300, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=EN, label=Table 3, caption=

Weights of the different criteria for experts

, figureFileSmall=null, figureFileBig=null, tableContent=
权重 专家1 专家2 专家3
工作绩效 0.195 1 0.195 1 0.181 8
工作年限 0.122 0 0.122 0 0.090 9
教育水平 0.243 9 0.243 9 0.242 4
专业 0.195 1 0.195 1 0.242 4
声誉 0.243 9 0.243 9 0.242 4
), ArticleFig(id=1167816052026127252, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=表3, caption=

专家的不同准则所占权重

, figureFileSmall=null, figureFileBig=null, tableContent=
权重 专家1 专家2 专家3
工作绩效 0.195 1 0.195 1 0.181 8
工作年限 0.122 0 0.122 0 0.090 9
教育水平 0.243 9 0.243 9 0.242 4
专业 0.195 1 0.195 1 0.242 4
声誉 0.243 9 0.243 9 0.242 4
), ArticleFig(id=1167816052084847510, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=EN, label=Table 4, caption=

Weights of each expert under different criteria and their comprehensive weights

, figureFileSmall=null, figureFileBig=null, tableContent=
专家 工作
绩效
工作
年限
教育
水平
专业 声誉 综合
权重
1 0.402 8 0.415 9 0.398 6 0.333 3 0.398 6 0.388 8
2 0.402 8 0.415 9 0.398 6 0.333 3 0.398 6 0.388 8
3 0.194 4 0.168 2 0.202 8 0.333 3 0.202 8 0.229 7
), ArticleFig(id=1167816052164539288, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149735803049390705, language=CN, label=表4, caption=

不同准则下的各专家权重及其综合权重

, figureFileSmall=null, figureFileBig=null, tableContent=
专家 工作
绩效
工作
年限
教育
水平
专业 声誉 综合
权重
1 0.402 8 0.415 9 0.398 6 0.333 3 0.398 6 0.388 8
2 0.402 8 0.415 9 0.398 6 0.333 3 0.398 6 0.388 8
3 0.194 4 0.168 2 0.202 8 0.333 3 0.202 8 0.229 7
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社会技术系统视角下城市地铁内涝事故致因定量分析
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邢金朵 1 , 杨威 1 , 张军 2
中国安全科学学报 | 公共安全 2024,34(11): 193-201
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中国安全科学学报 | 公共安全 2024, 34(11): 193-201
社会技术系统视角下城市地铁内涝事故致因定量分析
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邢金朵1 , 杨威1, 张军2
作者信息
  • 1 北京建筑大学 机电与车辆工程学院,北京 100044
  • 2 北京印刷学院 机电工程学院,北京 102699
  • 邢金朵 (1989—),女,河南宝丰人,博士,讲师,主要从事复杂系统动态风险评估、系统可靠性建模理论与应用等方面的研究。E-mail:

    张军,教授

Quantitative analysis of accident causes of urban subway waterlogging from perspective of complex socio-technical systems
Jinduo XING1 , Wei YANG1, Jun ZHANG2
Affiliations
  • 1 School of Mechanical-Electronic and Vehicle Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China
  • 2 School of Mechanical and Electrical Engineering,Beijing Institute of Graphic Communication,Beijing 102699,China
出版时间: 2024-11-28 doi: 10.16265/j.cnki.issn1003-3033.2024.11.2065
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为有效防控城市地铁内涝事故风险,提出一种将事故地图(AcciMap)模型、模糊层次分析法(FAHP)以及三角模糊决策实验室分析法(DEMATEL)-解释结构模型(ISM)方法融合的事故致因系统评价模型,定量挖掘城市地铁内涝事故致因及其耦合作用关系。通过AcciMap模型从系统层面遴选事故致因;用FAHP综合专家意见,确定专家权重;利用DEMATEL方法计算事故致因重要程度,采用ISM方法揭示因素间的相互关系,构造多级阶梯有向图。并以郑州地铁的内涝事故为例,验证提出的方法。结果表明:管理部门对特大灾害认识防范不足、监督部门监察职责履行不认真、运营部门应对处置不力是事故发生的核心致因要素,应予以重点关注,防止该社会技术系统失效。

社会技术系统  /  城市地铁内涝事故  /  事故致因  /  定量分析  /  决策实验室分析法(DEMATEL)  /  解释结构模型(ISM)

To effectively prevent and control the risk of subway waterlogging accidents,a quantitative analysis method integrating the AcciMap model,FAHP,and triangular fuzzy DEMATEL-ISM method was proposed to investigate the causes of urban subway waterlogging accidents and their coupling associations. Accident causation factors were selected from the system level through AcciMap model. FAHP was used to integrate expert opinions and determine expert weights. The DEMATEL method was used to calculate the importance of accident causes,ISM method was used to reveal the relationship between factors,and a multi-level ladder stepwise graph was constructed. The model was validated by waterlogging accidents on the Zhengzhou subway. The results showed that the core contributing factors leading to the incident were inadequate awareness and prevention measures of major disasters by the management department,the lack of diligence in supervision and inspection duties by regulatory authorities,and ineffective response and handling by operational departments. These factors can be paid special attention to prevent system failures.

complex socio-technical systems  /  urban subway waterlogging accidents  /  accident causes  /  quantitative analysis  /  decision making trial and evaluation laboratory (DEMATEL)  /  interpretative structural modeling (ISM)
邢金朵, 杨威, 张军. 社会技术系统视角下城市地铁内涝事故致因定量分析. 中国安全科学学报, 2024 , 34 (11) : 193 -201 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.2065
Jinduo XING, Wei YANG, Jun ZHANG. Quantitative analysis of accident causes of urban subway waterlogging from perspective of complex socio-technical systems[J]. China Safety Science Journal, 2024 , 34 (11) : 193 -201 . DOI: 10.16265/j.cnki.issn1003-3033.2024.11.2065
随着我国经济的快速发展,以地铁为主的城市轨道交通大跨步向前迈进。截至2023年年底,我国大陆地区总计有59个城市开通了城市轨道线路,总里程超过1.1万km[1]。然而,近年来,随强降雨等极端天气的频繁发生[2],城市地铁内涝事故频出,严重威胁到人民的生命健康财产安全,如2020年5月广州地铁因暴雨洪水倒灌进站全线停运[3];2021年郑州“7·20”事故造成14人死亡[4]。事故调查报告显示,城市地铁系统作为一个复杂的社会技术系统,事故成因众多繁杂,因此,亟需从系统理论角度评估城市地铁内涝灾害风险,量化事故成因,提出风险防范与管控的对策与建议。
目前,国内外学者对城市地铁内涝灾害开展了一系列相关研究,如王军武等[5]从地铁车站配置条件出发,识别暴雨内涝下影响地铁车站脆弱性的关键要素;宋英杰等[6]提出城市公交地铁双层交通网络模型,评估内涝条件交通网络的脆弱性;陈佳等[7]运用压力-状态-响应理论构建地铁车站脆弱性评价指标,并引入诱导有序加权平均算子赋权法来弱化极端值的影响;朱影含[8]从韧性角度评价内涝灾害下地铁站点的安全;闫绪娴等[9]从韧性角度利用Bow-Tie和贝叶斯方法分析事故致因;FORERO-ORTIZ[10]总结了地下交通系统洪水影响评估方法,指出目前研究在评估时大多采用功能主义的观点,依赖于洪水易发区与洪水事件,仅关注地铁站周围的表面区域;LIN Zhiyu等[11]数值模拟了复杂地铁站挡洪设施故障下的洪水入侵过程。现有研究大多是从物理层面评估城市地铁系统的脆弱性,在选择事故致因时大多集中于设备、环境以及运营管理层面,并未将地铁系统看作复杂社会技术系统,从政府、监督部门、事故进程等层次全面开展城市地铁内涝事故致因系统评估。
鉴于此,笔者拟考虑整个社会技术系统的影响因素及其之间的相互作用关系,采用事故地图(Accident Mapping,AcciMap)模型识别及遴选系统不同层面事故致因;用模糊层次分析法(Fuzzy Analytic Hierarchy Process,FAHP)评判参与打分的专家资格;运用三角模糊决策实验室分析法和解释结构模型(Decision Making Trial and Evaluation Laboratory-Interpretative Structural Modeling,DEMATEL-ISM)方法定量分析事故致因,挖掘事故致因之间的逻辑关系、演化路径和属性特征,以期为防控城市地铁内涝事故提供理论参考和实践指导。
对于社会技术系统事故致因,采用事故致因系统评价模型进行分析,如图1所示。首先,通过AcciMap模型从政府、监督机构、相关企业、运营管理、事故进程及设备和环境6个层次提取事故致因;在构建邻接矩阵时,为综合专家意见,基于FAHP考虑因素全面、主观影响程度低的优点,确定专家权重;利用三角模糊DEMATEL-ISM组合方法,识别复杂系统中因素之间的因果关系和层次结构,最终为制定和改善复杂系统风险防范策略提供科学依据。
AcciMap是RASMUSSEN[12]在1997年提出的一种用于分析社会技术系统事故致因的模型。通过构建AcciMap模型图,划分系统内影响事故发生的因素的层次,揭示整个系统中引起或未能防止事故发生的因素,有效防止过度关注事故的直接原因(如人为失误),从而发现引发这些错误或未能防止事故发生的系统缺陷[13]
FAHP在考虑模糊评价的基础上,面对涉及多目标、多标准决策分析时做出最优决策。为降低专家主观上的偏差,以专家的工作绩效、工作年限、教育水平、专业和声誉5个客观背景为准则,采用FAHP对参与打分的3位专家进行权重分配[14-16]
1) 建立层次结构模型。将决策目标、考虑因素(决策准则)和决策对象按它们之间的相互关系分为目标层、准则层和方案层。
2) 进行目标层和准则层之间的排序。由标准经验分数[16],根据专家背景评分。针对专家的某一背景准则所占的权重计算如下式:
W x = S x x m S x
式中: W x为某一专家背景准则x所得标准分数 S x在其所有背景准则中所占比例;m为背景准则个数。
3) 开展准则层和方案层之间的排序。为减少性质不同的诸因素相互比较的困难,采用一致性矩阵法对要素两两比较,同时用三角模糊数语言[17]表示比较的结果,生成判断矩阵 A i j = [ a i j ] n × n。其中, A i j为模糊正倒数矩阵,n为专家人数, a i j为下式的三角模糊数[18],其取值是根据每个准则下不同专家的得分两两比对,结合三角模糊数语言尺度表[17]进行选取。
a i j = [ 1,1 1 ] i = j   [ l i j m i j u i j ] = 1 u i j 1 m i j 1 l i j i j
采用几何均值归一化处理矩阵Aij
g i = j = 1 k a i j 1 k = j = 1 k l i j 1 k j = 1 k m i j 1 k j = 1 k u i j 1 k i = 1,2 k
计算各专家在相同准则下的模糊权重[17]
w f i = w f i l w f i m w f i u = g i i = 1 k g i = ( j = 1 k l i j ) 1 k i = 1 k ( j = 1 k l i j ) 1 k ( j = 1 k m i j ) 1 k i = 1 k ( j = 1 k m i j ) 1 k ( j = 1 k u i j ) 1 k i = 1 k ( j = 1 k u i j ) 1 k
式中 w f i为模糊值,用Yager指数法去模糊化。
w i = w f i l + 2 w f i m + w f i u 4
归一化处理 w i,得到 W ' i,即为专家i在某一准则下的权重比例。分别得到各准则下,不同专家所占权重。
W ' i = w i i = 1 n w i
4) 层次总排序。通过下式计算各位专家的综合权重[19]
W   i = W J i × W ' J i + W Y i × W ' Y i + W L i × W ' L i + W M i × W ' M i + W R i × W ' R i
式中: W   i为专家i的综合权重; W J i为专家i的工作绩效J在不同准则中的权重; W ' j i为专家i在工作绩效J下的单一权重;Y为工作年限;L为受教育程度;M为专业;R为声誉[15]
AcciMap模型属于定性分析方法,对于各因素的属性特征、因素间的逻辑关系及其系统性层级结构的解释存在一定的局限性。而DEMATEL可有效分析复杂问题中各因素在系统中的作用,确定主导因素并挖掘各因素的属性特征,但无法划分致因要素的层级[20];ISM能够确立系统单元间的交互关系,构建清晰的多级递阶层次结构模型[21]。但传统ISM方法将因素间的关系二元化处理,即有直接关系和无直接关系,就实际情况而言,系统因素关系较为复杂,不能简单二元化处理。因此,引入三角模糊数评价语言,在一定程度上,既提高了专家语言的准确性,又更清晰地表达影响因素间的关系强度[22]。利用三角模糊DEMATEL-ISM方法定量分析城市地铁系统内涝事故致因如下:
1) 通过AcciMap模型遴选事故致因,定义系统影响因素集合SS={S1S2,…,Si}。
2) 建立系统中因素的平均直接影响矩阵 E k。邀请m位专家依据两因素关系评价分值表[22]对因素集合S中两两因素之间关系分别进行打分得到影响因素评价分值矩阵 A k n × n,并将该矩阵中的评价分值 A i j k对应为三角模糊评价变量 ( l i j k m i j k u i j k ),用Yager指数方法去模糊化处理得到平均直接影响矩阵 E k
E i j k = l i j k + 2 m i j k + u i j k 4
式中 A i j k为第k位专家评价的因素SiSj影响关系强度的评价分值。
3) 通过加权求取平均直接影响矩阵E:
E i j = ω 1 E i j 1 + ω 2 E i j 2 + + ω m E i j m
式中: ω k为专家k的权重; E i j为加权后平均直接影响矩阵E中第i行第j列的元素。
4) 将行和的最大值p作为归一化基数,标准化处理E得到矩阵Q:
p = 1 m a x 1 i n j = 1 n E i j ( i j = 1,2 n )
Q = p · E
5) 求取综合影响关系矩阵F:
F = Q ( I - Q ) - 1
式中I为单位矩阵。
6) 计算各要素的影响度、被影响度、中心度和原因度[23]
影响度 D iF中各行值之和,表示因素i对所有其他因素的直接和间接影响。
D i = i = 1 n f i j
被影响度 C jF中各列值之和,表示因素j受到所有其他因素的直接或间接影响。
C j = j = 1 n f i j
i=j时,中心度 M i表示因素i对整个系统给予和接受的所有影响。
M i = D i + C j
原因度 R i表示因素i对整个系统的净影响程度。当 R i>0,表示该因素对其他因素影响较大,称为原因因素,反之,称为结果因素。
R i = D i - C j
7) 以 M i为横坐标, R i为纵坐标,绘制各因素的因果关系图。
8) 求取邻接矩阵B。邻接矩阵第i行第j列元素 b i j的取值规则如下式:
b i j = 1 r i j r i j = 1,2 n   0 r i j r i j = 1,2 n
式中r为矩阵F所有元素的均值与标准差之和。
K = ( I + B ) n + 1 = ( I + B ) n ( I + B ) 2 I + B
式中K为可达矩阵。
K中1表示某要素到另一要素之间存在着路径,0则不存在。可达集:某要素对应行中,包含有1的元素集合;先行集:某要素对应列中,包含有1的元素集合;交集:可达集和先行集的交集。
具有相同交集和可达集的因子被认为属于I级,将I级因子从因素集合中移除,重复这一过程,直到所有因素都分层后结束。
2021年7月20日,河南郑州地铁5号线04502次列车遭遇涝水灌入、失电迫停,经疏散救援,953人安全撤出、14人死亡,造成重大的人员伤亡和经济损失[23]。经调查,该事故是在极端暴雨影响下,政府部门、监督单位、运营单位等管理、应急、处置失误。采用事故致因系统评价模型深入推演其事故情景,定量分析系统失效原因。
根据《河南郑州“7·20”特大暴雨灾害调查报告》[4],利用AcciMap模型遴选事故致因要素,针对城市地铁内涝事故,调整通用AcciMap模型框架如图2所示。划分相关部门单位按照所属层级、系统作用,事故致因见表1。按照因素间的因果关系构建该地铁内涝事故的AcciMap模型,如图3所示。
在案例分析中邀请2位从事相关研究的高校教师和1位地铁运营管理人员,利用FAHP确定专家权重。首先,建立层次结构模型如图4所示。根据背景准则标准经验分数表[16],专家在各准则下得分见表2,其中,声誉的评分来自其他专家的认可和评价。
其次,进行目标层和准则层之间的排序。由表2专家在不同准则下的得分,根据式(1),计算每个专家的不同准则所占的经验权重,如对专家1,工作绩效所占权重 W J 1为8/(8+5+10+8+10)=0.195 1,计算结果见表3
最后,进行层次总排序。由表2专家分数,结合三角模糊语言尺度构造对比判断矩阵,对矩阵进行式(2)—式(6)的计算得到各专家相互之间在每个准则下的权重。由式(7)得专家最终的综合权重,计算结果见表4
由专家意见,对郑州地铁内涝事故致因两两之间进行评价打分得到直接影响矩阵,并通过式(8)—式(12)、式(17)和式(18)得到可达矩阵K
K = 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
K通过集合方法划分系统影响要素的区域。由于因素较多,影响关系复杂,为避免所得层次结构模型过于繁复,在进行层次划分时,选择简化已具有邻接二元关系要素间的越级二元关系,城市地铁内涝事故致因多级递阶结构模型如图5所示。
在综合影响关系矩阵F基础上,经式(13)—式(16)计算结果绘制地铁内涝事故致因原因-中心度如图6所示。
1) DEMATEL-ISM模型分析。通过AcciMap模型遴选的17个事故致因,分为7个子层级、3个大层级,分别为近邻致因、过渡致因和本质致因。近邻致因主要包括S10S12S15S17这4项因素,是事故的直接原因和宏观表现。过渡致因包括S2S3S4S6S7S8S9S11S14S16共11项因素,此类因素相互作用,在事故体系中承上启下,是事故演化路径上的关键节点。该因素集的S6S7S8位于AcciMap模型中的技术与运营层面,作为地铁系统的直接管理者,事故引发链在这一层面较容易拦截,阻断事故链传播;相关企业的监督职责履行不到位,导致设备物理层面出现问题,也是造成事故发生的间接原因。本质致因包括S1S5S13这3项因素,是事故的深层原因,能够直接影响过渡致因和近邻致因,需在风险防控中加以重视。
2) DEMATEL原因-中心度分析。由图6知:将所有致因要素分为4类,第1类为强原因因子集(I区),包括S1S2S3S4S8,这类因子对其他结果型因子及整个系统都有显著影响,其中,以S1灾害风险意识不强最为显著,因此,要加强政府层面灾害风险防范意识。另外S3相关监管部门的不作为也是本次事故的重要强原因型因子,监督部门职责履行不认真、检查不到位,可能会导致其他部门产生侥幸心理,而最终酿成严重后果。第2类为弱原因因子集(Ⅱ区),主要包括S5S6S13S17,这类因子对事故的发生有重要影响,但相比I区偏弱。其中,S13暴雨作为天灾无法避免,但通过加强科学的气象预报,采取有效预防、应急措施等,降低事故发生的概率。第3类为弱结果因子集Ⅲ区),主要包括S11S15,这类因子是其他原因型因子综合作用的结果,间接造成最终灾难性后果的发生。第4类为强结果因子集(Ⅳ区),这类因子也是其他原因型因子综合作用的结果,属于事故发生的直接原因,影响重大,其中,S12列车迫停、乘客被困、疏散中断其原因度最低,对其他因素影响较小,但中心度大,表明该因素是在众多致因作用下而导致的最终结果,由于列车迫停、乘客被困、疏散中断,导致了人员的伤亡,根据实际情况制定具体的应急预案并加强演练磨合,降低事故带来的损失。另外,中心度越大,其影响程度越大,需重点关注Ⅰ区的强原因因子集及Ⅳ区的强结果因子集。
1) 融合AcciMap模型、FAHP以及三角模糊DEMATEL-ISM方法,并通过城市地铁内涝事故证明该模型能够为预防社会技术系统失效提供一定的理论参考建议。同时,ISM与DEMATEL原因-中心度分析结果在因素间的因果关系上一致,互相验证,证明了该组合模型的合理性。
2) 灾害风险意识不强、防灾减灾气象保障水平不足以及暴雨是事故发生的本质致因,同时此三者的原因度都处于较高水平要予以重点关注。列车迫停、乘客被困、疏散中断作为中心度最高的结果因素,与实际一致,是其他因素综合作用的结果,可通过增强全社会风险意识和自救互救能力,降低事故后果的严重性。
3) 通过与事故调查报告的比对,其所得结论与事故调查内容基本一致。因事故致因的选取主要依托于郑州“7·20”事故调查报告,主要针对郑州地铁技术系统模型,若推广至不同城市,需进一步调整和论证具体情况。
  • 北京市教育委员会科研计划一般项目(KM202310016003)
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2024年第34卷第11期
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doi: 10.16265/j.cnki.issn1003-3033.2024.11.2065
  • 接收时间:2024-05-15
  • 首发时间:2025-07-09
  • 出版时间:2024-11-28
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  • 收稿日期:2024-05-15
  • 修回日期:2024-08-21
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北京市教育委员会科研计划一般项目(KM202310016003)
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    1 北京建筑大学 机电与车辆工程学院,北京 100044
    2 北京印刷学院 机电工程学院,北京 102699
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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