Article(id=1149738957254082910, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, articleNumber=1003-3033(2024)04-0050-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.04.1452, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1697212800000, receivedDateStr=2023-10-14, revisedDate=1705507200000, revisedDateStr=2024-01-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048728525, onlineDateStr=2025-07-09, pubDate=1714233600000, pubDateStr=2024-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048728525, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048728525, creator=13701087609, updateTime=1752048728525, updator=13701087609, issue=Issue{id=1149738954913661267, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='4', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048727968, creator=13701087609, updateTime=1756468927830, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1168278616925286857, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278616925286858, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=50, endPage=57, ext={EN=ArticleExt(id=1149738958348796279, articleId=1149738957254082910, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Accident causal analysis of object strike in hydropower project construction based on text mining, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To clarify the accident causes and their association rules of strike accidents in hydropower project construction,specific accident prevention strategies were proposed to curb accidents at source. Based the selected object strike accidents during hydropower construction process,14 accident causal parameters were summarized using text mining from four perspectives including worker,machine,environment and management. Moreover,an importance analysis model was proposed by coupling association rules and the DEMATEL approach to analyze the interrelated relationship between causes and determine major accident causes. The results showed that the accident causes of object strikes in hydropower projects were closely related to each other,among which inadequate safety supervision and inspection,illegal operations,insufficient safety protection,and weak safety awareness directly affected the accident's occurrence. Furthermore,the deeper causing factors of accidents were incomplete on-site cleaning,insufficient disclosure of safety technology,and inadequate safety education. Weak safety awareness ranked first in terms of centrality and had the greatest influence on the accident causation system,thereby the above causative factors should be emphasized in project construction.

, correspAuthors=Bo SHAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xiazhong ZHENG, Yicheng LIU, Bo SHAO, Shuo WANG, Shan'gang KE), CN=ArticleExt(id=1149738967576265331, articleId=1149738957254082910, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于文本挖掘的水电工程施工物体打击事故致因分析, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为探明水电工程施工过程中物体打击事故的致因及作用关系,提出针对性的事故预防措施,从源头遏制事故发生。遴选水电工程施工物体打击事故调查报告,运用文本挖掘从人、机、环、管4个方面提取14项事故致因。融合关联规则、决策试验和评价实验室(DEMATEL)方法,厘清致因因素间的关联关系,构建重要度分析模型,确定事故近端致因因素。结果表明:水电工程物体打击事故各致因间联系紧密,其中,安全监督检查不到位、违章作业、安全防护措施不完备、安全意识淡薄等致因因素对事故发生具有直接影响作用。现场清理不彻底、安全技术交底不到位、安全教育培训不到位等是诱发事故的深层影响因素,且安全意识淡薄的中心度排名第一,对事故致因系统的影响较大,在实际施工中应着重考虑上述致因因素。

, correspAuthors=邵波, authorNote=null, correspAuthorsNote=
**邵波(1990—),男,湖北孝感人,博士,副教授,主要从事安全管理、风险管理等方面的研究。E-mail:
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郑霞忠 (1963—),男,湖北鄂州人,博士,教授,主要从事安全科学与工程方面的研究。E-mail:

邵波 副教授

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Accident theme features words

, figureFileSmall=null, figureFileBig=null, tableContent=
主题 特征词
1 现场、管理、事故、落实、责任、履行、职责、督促、规章制度
2 防范、措施、流于形式、未能、支架、扣件、支护、横撑、溜槽、承台
3 作业、人员、违章、操作、违反、规程、吊装、吊柱、放置、钢管、挖掘机
4 现场、爆破、岩块、围岩、块石、施工、危石、裂隙、隐患、电杆、缺位、节理
5 事故隐患、滑坡、重视、及时、发现、井沟、涉事、盖板、消除、施工
6 作业、交叉、现场、拖吊、拉线、挂钩、矩管、发生、风道、分包
7 玄武岩、岩类、致密、坚硬、倾角、斑状、岩石、导致、扰动、混凝土、事故
8 安全意识、盲目、缺乏、提升、坠落、放松、拆除、致使、人员、施工
9 交底、技术、操作、规程、细则、断裂、严格、执行、制定、监理
10 维护、保养、年限、较长、断裂、冲击、磨损、长时间、联结、螺栓、料斗、托轮
11 检查、安全、焊接、隐患、人员、牢固、固定、排查、整治、冒险
12 教育、培训、意识、擅自、指挥、过程、规定、现场、到位、监理
13 经验、人员、安全、意识、脱落、缺乏、失误、落实、导致、施工、发生
14 清理、岩石、到位、部位、现场、围岩、异常、施工、导致、钻孔
), ArticleFig(id=1168150814137791420, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=CN, label=表1, caption=

事故主题特征词

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主题 特征词
1 现场、管理、事故、落实、责任、履行、职责、督促、规章制度
2 防范、措施、流于形式、未能、支架、扣件、支护、横撑、溜槽、承台
3 作业、人员、违章、操作、违反、规程、吊装、吊柱、放置、钢管、挖掘机
4 现场、爆破、岩块、围岩、块石、施工、危石、裂隙、隐患、电杆、缺位、节理
5 事故隐患、滑坡、重视、及时、发现、井沟、涉事、盖板、消除、施工
6 作业、交叉、现场、拖吊、拉线、挂钩、矩管、发生、风道、分包
7 玄武岩、岩类、致密、坚硬、倾角、斑状、岩石、导致、扰动、混凝土、事故
8 安全意识、盲目、缺乏、提升、坠落、放松、拆除、致使、人员、施工
9 交底、技术、操作、规程、细则、断裂、严格、执行、制定、监理
10 维护、保养、年限、较长、断裂、冲击、磨损、长时间、联结、螺栓、料斗、托轮
11 检查、安全、焊接、隐患、人员、牢固、固定、排查、整治、冒险
12 教育、培训、意识、擅自、指挥、过程、规定、现场、到位、监理
13 经验、人员、安全、意识、脱落、缺乏、失误、落实、导致、施工、发生
14 清理、岩石、到位、部位、现场、围岩、异常、施工、导致、钻孔
), ArticleFig(id=1168150814213288893, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=EN, label=Tab.2, caption=

Causal factors of object strike accidents

, figureFileSmall=null, figureFileBig=null, tableContent=
代码 致因因素 代码 致因因素
C1 现场管理失职 C8 安全意识淡薄
C2 安全防护措施
不完备
C9 安全技术交底
不到位
C3 违章作业 C10 维护保养不到位
C4 作业环境不佳 C11 安全监督检查
不到位
C5 隐患排查治理
不力
C12 安全教育培训
不到位
C6 交叉作业 C13 施工经验不足
C7 地质条件不良 C14 现场清理不彻底
), ArticleFig(id=1168150814267814846, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=CN, label=表2, caption=

物体打击事故致因因素

, figureFileSmall=null, figureFileBig=null, tableContent=
代码 致因因素 代码 致因因素
C1 现场管理失职 C8 安全意识淡薄
C2 安全防护措施
不完备
C9 安全技术交底
不到位
C3 违章作业 C10 维护保养不到位
C4 作业环境不佳 C11 安全监督检查
不到位
C5 隐患排查治理
不力
C12 安全教育培训
不到位
C6 交叉作业 C13 施工经验不足
C7 地质条件不良 C14 现场清理不彻底
), ArticleFig(id=1168150814330729407, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=EN, label=Tab.3, caption=

Top 6 strong association rules

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后项 前项 支持度 置信度 作用度
C11 C2C12 0.111 0.833 1.875
C8 C11C13 0.111 0.833 1.364
C8 C14 0.102 0.818 1.339
C8 C7C13 0.102 0.818 1.339
C8 C1C13 0.102 0.818 1.339
C8 C2C13 0.194 0.81 1.325
), ArticleFig(id=1168150814389449664, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=CN, label=表3, caption=

排名前6的强关联规则

, figureFileSmall=null, figureFileBig=null, tableContent=
后项 前项 支持度 置信度 作用度
C11 C2C12 0.111 0.833 1.875
C8 C11C13 0.111 0.833 1.364
C8 C14 0.102 0.818 1.339
C8 C7C13 0.102 0.818 1.339
C8 C1C13 0.102 0.818 1.339
C8 C2C13 0.194 0.81 1.325
), ArticleFig(id=1168150814448169921, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=EN, label=Tab.4, caption=

Direct impact matrix M

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致因因素 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14
C1 0.00 0.47 0.62 0.00 0.41 0.00 0.00 0.62 0.00 0.00 0.59 0.44 0.00 0.00
C2 0.00 0.00 0.42 0.00 0.00 0.62 0.00 0.00 0.00 0.00 0.54 0.00 0.42 0.00
C3 0.40 0.40 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.00 0.54 0.00 0.40 0.00
C4 0.00 0.00 0.00 0.00 0.00 0.00 0.56 0.64 0.00 0.00 0.00 0.00 0.00 0.00
C5 0.00 0.44 0.44 0.00 0.00 0.00 0.00 0.58 0.00 0.00 0.53 0.00 0.00 0.00
C6 0.00 0.61 0.50 0.00 0.00 0.00 0.00 0.61 0.00 0.00 0.56 0.00 0.44 0.00
C7 0.00 0.46 0.00 0.58 0.00 0.00 0.00 0.63 0.00 0.00 0.00 0.00 0.46 0.00
C8 0.00 0.47 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.00 0.47 0.00
C9 0.50 0.50 0.71 0.00 0.57 0.00 0.00 0.50 0.00 0.00 0.71 0.64 0.00 0.00
C10 0.00 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C11 0.42 0.56 0.58 0.00 0.00 0.00 0.00 0.56 0.00 0.00 0.00 0.00 0.00 0.00
C12 0.52 0.41 0.59 0.00 0.45 0.00 0.00 0.48 0.00 0.00 0.62 0.00 0.00 0.00
C13 0.00 0.51 0.51 0.00 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00
C14 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.82 0.00 0.00 0.45 0.00 0.55 0.00
), ArticleFig(id=1168150814536250306, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=CN, label=表4, caption=

直接影响矩阵M

, figureFileSmall=null, figureFileBig=null, tableContent=
致因因素 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14
C1 0.00 0.47 0.62 0.00 0.41 0.00 0.00 0.62 0.00 0.00 0.59 0.44 0.00 0.00
C2 0.00 0.00 0.42 0.00 0.00 0.62 0.00 0.00 0.00 0.00 0.54 0.00 0.42 0.00
C3 0.40 0.40 0.00 0.00 0.00 0.00 0.00 0.60 0.00 0.00 0.54 0.00 0.40 0.00
C4 0.00 0.00 0.00 0.00 0.00 0.00 0.56 0.64 0.00 0.00 0.00 0.00 0.00 0.00
C5 0.00 0.44 0.44 0.00 0.00 0.00 0.00 0.58 0.00 0.00 0.53 0.00 0.00 0.00
C6 0.00 0.61 0.50 0.00 0.00 0.00 0.00 0.61 0.00 0.00 0.56 0.00 0.44 0.00
C7 0.00 0.46 0.00 0.58 0.00 0.00 0.00 0.63 0.00 0.00 0.00 0.00 0.46 0.00
C8 0.00 0.47 0.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.41 0.00 0.47 0.00
C9 0.50 0.50 0.71 0.00 0.57 0.00 0.00 0.50 0.00 0.00 0.71 0.64 0.00 0.00
C10 0.00 0.50 0.00 0.00 0.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
C11 0.42 0.56 0.58 0.00 0.00 0.00 0.00 0.56 0.00 0.00 0.00 0.00 0.00 0.00
C12 0.52 0.41 0.59 0.00 0.45 0.00 0.00 0.48 0.00 0.00 0.62 0.00 0.00 0.00
C13 0.00 0.51 0.51 0.00 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00
C14 0.00 0.64 0.00 0.00 0.00 0.00 0.00 0.82 0.00 0.00 0.45 0.00 0.55 0.00
), ArticleFig(id=1168150814615942083, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=EN, label=Tab.5, caption=

Results of solving the importance model

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Ci V Y U J q b
C1 1.571 1.012 2.583 0.558 5 原因
C2 0.830 2.873 3.702 -2.041 3 结果
C3 1.145 2.664 3.809 -1.519 2 结果
C4 0.548 0.163 0.711 0.386 13 原因
C5 0.950 0.608 1.558 0.342 10 原因
C6 1.281 0.581 1.862 0.700 9 原因
C7 0.910 0.158 1.068 0.753 12 原因
C8 0.866 3.028 3.894 -2.162 1 结果
C9 2.147 0.000 2.147 2.147 6 原因
C10 0.458 0.000 0.458 0.458 14 原因
C11 1.064 2.580 3.644 -1.516 4 结果
C12 1.551 0.369 1.920 1.182 8 原因
C13 0.834 1.244 2.078 -0.410 7 结果
C14 1.123 0.000 1.123 1.123 11 原因
), ArticleFig(id=1168150814683050948, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1149738957254082910, language=CN, label=表5, caption=

重要度模型求解结果

, figureFileSmall=null, figureFileBig=null, tableContent=
Ci V Y U J q b
C1 1.571 1.012 2.583 0.558 5 原因
C2 0.830 2.873 3.702 -2.041 3 结果
C3 1.145 2.664 3.809 -1.519 2 结果
C4 0.548 0.163 0.711 0.386 13 原因
C5 0.950 0.608 1.558 0.342 10 原因
C6 1.281 0.581 1.862 0.700 9 原因
C7 0.910 0.158 1.068 0.753 12 原因
C8 0.866 3.028 3.894 -2.162 1 结果
C9 2.147 0.000 2.147 2.147 6 原因
C10 0.458 0.000 0.458 0.458 14 原因
C11 1.064 2.580 3.644 -1.516 4 结果
C12 1.551 0.369 1.920 1.182 8 原因
C13 0.834 1.244 2.078 -0.410 7 结果
C14 1.123 0.000 1.123 1.123 11 原因
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基于文本挖掘的水电工程施工物体打击事故致因分析
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郑霞忠 1, 2 , 刘奕成 2 , 邵波 1, 2, ** , 王硕 2 , 柯善钢 2
中国安全科学学报 | 安全工程技术 2024,34(4): 50-57
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中国安全科学学报 | 安全工程技术 2024, 34(4): 50-57
基于文本挖掘的水电工程施工物体打击事故致因分析
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郑霞忠1, 2 , 刘奕成2, 邵波1, 2, ** , 王硕2, 柯善钢2
作者信息
  • 1 三峡大学 水电工程施工与管理湖北省重点实验室,湖北 宜昌 443002
  • 2 三峡大学 水利与环境学院,湖北 宜昌 443002
  • 郑霞忠 (1963—),男,湖北鄂州人,博士,教授,主要从事安全科学与工程方面的研究。E-mail:

    邵波 副教授

通讯作者:

**邵波(1990—),男,湖北孝感人,博士,副教授,主要从事安全管理、风险管理等方面的研究。E-mail:
Accident causal analysis of object strike in hydropower project construction based on text mining
Xiazhong ZHENG1, 2 , Yicheng LIU2, Bo SHAO1, 2, ** , Shuo WANG2, Shan'gang KE2
Affiliations
  • 1 Hubei Key Laboratory of Construction and Management in Hydropower Engineering,China Three Gorges University,Yichang Hubei 443002,China
  • 2 College of Hydraulic & Environmental Engineering,China Three Gorges University,Yichang Hubei 443002,China
出版时间: 2024-04-28 doi: 10.16265/j.cnki.issn1003-3033.2024.04.1452
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为探明水电工程施工过程中物体打击事故的致因及作用关系,提出针对性的事故预防措施,从源头遏制事故发生。遴选水电工程施工物体打击事故调查报告,运用文本挖掘从人、机、环、管4个方面提取14项事故致因。融合关联规则、决策试验和评价实验室(DEMATEL)方法,厘清致因因素间的关联关系,构建重要度分析模型,确定事故近端致因因素。结果表明:水电工程物体打击事故各致因间联系紧密,其中,安全监督检查不到位、违章作业、安全防护措施不完备、安全意识淡薄等致因因素对事故发生具有直接影响作用。现场清理不彻底、安全技术交底不到位、安全教育培训不到位等是诱发事故的深层影响因素,且安全意识淡薄的中心度排名第一,对事故致因系统的影响较大,在实际施工中应着重考虑上述致因因素。

文本挖掘  /  水电工程  /  物体打击  /  事故致因  /  关联规则  /  决策试验和评价实验室(DEMATEL)方法

To clarify the accident causes and their association rules of strike accidents in hydropower project construction,specific accident prevention strategies were proposed to curb accidents at source. Based the selected object strike accidents during hydropower construction process,14 accident causal parameters were summarized using text mining from four perspectives including worker,machine,environment and management. Moreover,an importance analysis model was proposed by coupling association rules and the DEMATEL approach to analyze the interrelated relationship between causes and determine major accident causes. The results showed that the accident causes of object strikes in hydropower projects were closely related to each other,among which inadequate safety supervision and inspection,illegal operations,insufficient safety protection,and weak safety awareness directly affected the accident's occurrence. Furthermore,the deeper causing factors of accidents were incomplete on-site cleaning,insufficient disclosure of safety technology,and inadequate safety education. Weak safety awareness ranked first in terms of centrality and had the greatest influence on the accident causation system,thereby the above causative factors should be emphasized in project construction.

text mining  /  hydropower project construction  /  object strike  /  accident causes  /  association rules  /  decision making trial and evaluation laboratory(DEMATEL) approach
郑霞忠, 刘奕成, 邵波, 王硕, 柯善钢. 基于文本挖掘的水电工程施工物体打击事故致因分析. 中国安全科学学报, 2024 , 34 (4) : 50 -57 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1452
Xiazhong ZHENG, Yicheng LIU, Bo SHAO, Shuo WANG, Shan'gang KE. Accident causal analysis of object strike in hydropower project construction based on text mining[J]. China Safety Science Journal, 2024 , 34 (4) : 50 -57 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1452
事故预防一直是安全管理工作的重点、难点,有效的事故预防措施主要依赖于研究历史事故,以此给事后控制提供便利[1]。物体打击是工程施工领域中备受关注的高风险安全事故之一,易导致重大生命财产损失,在劳动力、施工机具、物料投入较多的工程施工中多发频发,尤其是交叉作业施工过程[2-3]。而水电工程多在深山峡谷,施工技术复杂,作业空间有限,施工要素更加密集,导致物体打击事故更为频繁,并且相较于其他工程领域危害更大,影响更为恶劣[4-6]。为预防水电工程施工物体打击事故,降低施工安全风险,防止事故带来的危害,需探明水电工程施工物体打击事故致因和挖掘致因间的作用关系。
对于水电工程施工物体打击事故的致因研究,目前学者们主要利用事故致因模型来解释事故发生的机制,厘清事故致因间的逻辑关系,分析事故致因过程[7]。ZHENG Xiazhong等[8]利用人为因素分析及分类系统探索水电工程事故致因与人为因素之间的联系。传统的事故致因分析多依赖研究人员经验,加之事故文本多为非结构化数据,对事故分析的准度和效率提出很大挑战。为应对这个问题,部分学者开始采用文本挖掘等智能分析方法研究事故致因,如陈述等[9]利用短语提取技术从非结构化文本中挖掘水电工程施工安全隐患部位与隐患类型的关联规则,发现在边坡处极易发生物体打击事故;王仁超等[10]结合RoBERTa-wwm-CNN混合深度学习模型,实现水电工程施工安全隐患文本的智能分类,揭示了物体打击事故的分布特征,研究发现,“坝段”“平台”“尾水段”为物体打击隐患的多发部位;CHEN Shu等[11]采用基于卷积神经网络的智能文本分类方法,从水电事故报告中提取文本信息,并结合关联规则和桑基图探究物体打击事故与其发生部位之间的关系。综上,学者们的研究大多围绕水电工程物体打击事故与隐患部位之间的联系,对于水电工程施工中物体打击事故致因尚缺乏系统的研究,并且物体打击事故致因因素间的作用关系没有得到充分展现。
鉴于此,笔者拟综合考虑施工现场事故过程,采用文本挖掘方法提取水电工程施工物体打击事故特征词项,挖掘物体打击事故致因主题,结合Apriori算法挖掘因素间的关联规则,量化分析因素间的逻辑关系,全面系统地分析事故致因,以期为水电工程施工安全管理提供理论依据。
首先,收集水电工程施工物体打击事故报告,预处理事故报告文本,将预处理后的文本语料导入潜在狄利克雷分布(Latent Dirichlet Allocation,LDA)主题模型,提取得到物体打击事故主题和事故特征词,归纳总结出物体打击事故致因因素;然后,遍历事故报告集,挖掘致因因素间的关联规则,设定合理阈值得到强关联规则,并按作用度对关联规则进行排序;最后,为探究各致因因素间的作用关系,采用决策试验和评价实验室(Decision Making Trial and Evaluation Laboratory,DEMATEL)方法分析各致因因素的重要度,计算得出各致因因素的原因-结果属性,达到挖掘隐含信息的目的。具体研究流程如图1所示。
水电工程施工事故多为大型伤亡事故,事故发生后企业和当地政府会根据实际情况调查统计伤亡人数、经济损失及社会影响等,总结出的事故调查报告反映了事故发生的原因及经过,相较于其他事故文本更具代表性,故从某大型水电企业获得物体打击事故调查报告108份,作为文本挖掘的语料,报告包含近些年较新的事故数据,确保后续事故致因分析的准确性。
数据预处理主要包括jieba分词、去停用词及归并近义词项。采用jieba分词中可以把句子精确切开且适合文本分析的精确模式,同时,建立水电工程物体打击事故自定义词典,以防部分专业词汇被错误切分,进而提高结果的准确性。停用词是文本中经常出现的术语,如标点符号、连词和代词等,对于文本分析毫无意义甚至会对后续的数据分析造成干扰,故需进行停用词过滤,文中停用词表主要来源于百度停用词库。水电工程文本数据中存在许多意思含义相近的词汇,如“撞击”“碰撞”“相撞”可统称为“碰撞”,为精简分词结果,归并处理文本中相近含义词项。
LDA主题模型是一种从文本中抽取主题的主题挖掘模型,2003年由BLEI等[12]提出,又被称为3层贝叶斯概率模型,包括文档、主题、词3层。LDA的基本流程是生成以文档为条件的主题概率分布和以主题为条件的词概率分布,它可以充分挖掘文档词项间的潜在联系,帮助人们从大量文本语料中更高效地获取潜在主题或中心思想,从而对文本内容进行划分归类,达到快速获取有效信息的目的。LDA采用语义分析技术,利用文本词项共现特征,通过无监督方式挖掘出文档中隐含的抽象主题,基本思想是:一篇文档隐含多个主题,一个主题由多个词项组成,通过不间断迭代模拟文档生成过程,进而将文档表示为潜在主题的随机混合,最后每个主题通过词分布进行表征,即潜在主题是由文档-词概率分布和主题-词概率分布中得出。
LDA生成流程:由于主题的先验分布服从参数ɑ控制的Dirichlet分布,则文档m的主题分布θm可表示为:θm=Dirichlet(ɑ);从文档m中任选一个主题k,主题k中的词分布也服从由参数β控制的Dirichlet分布,则主题k的词分布ψk可表示为:ψk=Dirichlet(β);对于第m个文档的主题k的词语分布ψk中的第n个词,根据θm可得到它的主题编号分布zmn,即zmn=Multi(θm);对于某一主题下的词概率分布可由ψk取样获取,根据主题编号k=zmn可得wm.n=Multi(ψzmn)。其中,ɑ表示文档-主题分布的Dirichlet参数;β表示主题-词语分布的Dirichlet参数;zmn表示第m篇文档第n个词的主题编号;wm.n表示第m篇文档第n个词项。
在建立LDA主题模型时,模型主题数目需得到科学合理的设置。由于不同数量的主题数可能会导致所挖掘的语料库结构完全不同,主题数目过小会导致模型过于粗糙,主题之间区分度不够;主题数目过大则会导致模型过拟合,主题之间产生重叠。因此,为确定主题数目,引入困惑度指标来确定合理的主题数目,困惑度的定义可理解为主题模型对于某一特定文档属于某一主题的不确定程度,所以困惑度越小表明该模型的区分度越高,模型结构越好,主题数目越优。困惑度计算公式为:
X = e x p - m = 1 R l o g d ( P ( w m ) ) m = 1 R H m
式中:X为困惑度;R为文档总数;d为语料库中的测试集;P(wm)为第m篇文档每个单词的概率;Hm为第m篇文档词项总数。
将文本预处理后的事故数据通过Wordcloud工具包可视化展示,得到水电工程物体打击事故词云图,如图2所示。词云图中词语字体大小与词频高低成正比,同时词语相距越近联系越大。
将预处理得到的事故数据文本导入LDA主题模型,困惑度随主题数的变化曲线如图3所示。由图3可知:当主题数目K为14时,此时主题重叠现象较小,说明此时模型训练效果良好,通过困惑度检验最终得到最优主题数为14。
输入事故文档集和最优主题数,通过python编码构建LDA模型,抽取水电工程施工物体打击事故潜在主题。在最优主题数的基础上通过LDA主题模型得到各个主题下对应的主题特征词,从主题致因分析视角出发,剔除各个主题下词义含糊、语义范围较大的词项,保留符合事故致因过程的特征词项,最终提取的事故主题特征词见表1。为加强事故致因分析的科学性和系统性,在事故特征词性上保留有名词和动词以及少量形容词,以便完善主题特征,体现事故主题的显性性质;从直接和间接2个原因层面梳理特征词项,便于提高后续致因因素分析的逻辑性。
为探明水电工程作业中物体打击事故致因因素间的影响关系和作用逻辑,根据文本挖掘的事故主题特征词结果,回归事故报告语料,结合前人研究成果,合并含义相近的致因,依据全面适用原则合理确定事故致因因素,致因因素之间相互独立且互不影响,最终归纳总结出事故致因因素14个。其中,全面涵盖了人、机、环、管 4个方面的作用因素,达到了系统性分析目的。事故致因因素结果见表2
关联规则是数据挖掘中识别要素间潜在关联关系的一种重要方法,它能够反映一种事物与其他事物之间的相互依存关系,从而量化因素之间的关联程度[13]。目前普遍使用Apriori算法挖掘数据之间的关联规则,其原理是基于逐层搜索的迭代方法挖掘项集之间的关联规则[14]。文中结合Apriori算法挖掘14项致因因素间的关联规则,并构建强关联规则分析事故致因。
记总项目数据集D={T1T2,…,Tn}是所有项目Ti构成的集合,设AB为全集D的项目子集,并且AB≠Ø,则关联规则可表示为AB的蕴含式,A为前项,B为后项。用支持度S和置信度I衡量2因素间的关联性强弱,作用度L表示2因素间的相关性。
项目集A的支持度是指项目集A在总项目集D中占比,反映的是AD中出现的概率大小。而关联规则AB的支持度则表示为项目集A和项目集B在全集D中同时出现的概率大小,关联规则AB的支持度计算式为:
S ( A ) = P ( A )
S ( A B ) = P ( A B )
关联规则AB的置信度是指项目集A和项目集B在全集D中同时出现的概率与项目集AD中出现概率的比值,反映的是A出现的情况下B出现的概率,可以衡量AB之间关联性的强弱。关联规则AB的置信度计算式为:
I ( A B ) = S ( A B ) S ( A ) = P ( A B ) P ( A )
关联规则AB的作用度是指AB的置信度与B的支持度的比值,反映项目集A的出现对项目集B出现概率的影响程度。关联规则AB的作用度计算式为:
L ( A B ) = I ( A B ) S ( B ) = P ( A B ) P ( A ) · P ( B )
设置支持度与置信度的阈值,若关联规则AB的支持度与置信度均不劣于支持度与置信度的阈值,称强关联规则;反之为弱关联规则。作用度反映关联规则中AB的相关性:当L=1时,AB没有相关性,强关联规则无效;当L<1时,AB呈负相关性,A的出现抑制了B的出现;当L>1时,AB呈正相关性,A的出现促进了B的出现,则强关联规则有效。
将108份水电工程物体打击事故报告作为基础资料,构建物体打击事故关联规则数据集。首先,通过Apriori算法挖掘每对致因因素的关联规则;其次,通过设置参数阈值获取有分析价值的强关联规则;最好通过人工调试最终设定最小支持度为0.1,最小置信度为0.4,最小作用度为0.7,经数据挖掘生成171条强关联规则用作物体打击事故致因关联性分析,其中,97.7%的关联规则的支持度介于0.1~0.6之间,78.9%的关联规则作用度>1,54.4%的关联规则的置信度>0.5%,这表明水电工程施工物体打击事故致因间复杂的耦合性和关联性[15]
设定最小支持度为0.1,最小置信度为0.8,按作用度获得排名前6的关联规则作为代表性强关联规则进行分析,见表3。其中,强关联规则{C2/C12}⇒{C11} 作用度最高,说明致因C2C12导致致因C11出现的概率极高。结合事故报告和专家经验分析强关联规则的真实性,{C1/C13}⇒{C8}关联规则表示当工人施工经验不足时很大概率会出现安全意识淡薄,从而导致事故发生。研究表明:在实际作业人员中农民工参与占比达到40%,由于个别农民工的文化水平不高施工经验不足,安全意识不强,进行违章操作导致物体打击事故发生,符合事故致因过程。同理,其他强关联规则也得到有效性验证。
基于各因素强关联规则提取,采用DEMATEL方法计算致因因素重要度。DEMATEL方法是一种综合运用图论与矩阵进行系统要素分析的方法,能够极大简化复杂问题的分析难度。该方法可确定各因素在系统中的地位、因素间因果关系以及主次关系,抓住问题中的主要矛盾。具体操作流程是在确定两两因素之间影响程度的直接影响矩阵的基础上,根据矩阵运算求得各因素的中心度和原因度,在此基础上分析各致因因素。
1) 构建直接影响矩阵M。通过关联规则揭示各致因因素间的关联关系,得到各因素间的置信度,置信度与DEMATEL方法中直接影响矩阵中的数值含义存在一定程度的共性,都可以表示各因素间影响程度的大小,因此,把置信度转化为直接影响矩阵中行元素对列元素的影响值,即关联规则中前项对后项的影响程度,再按照DEMATEL方法规定,直接影响矩阵中主对角线影响值设为0。直接影响矩阵M表4
2) 规范化直接影响矩阵N。归一化处理所得的直接影响矩阵,即将矩阵内各数值转化为[0,1]区间内的数值,归一化处理方法如下式:
N = 1 m a x 1 i n j = 1 n M i j M
3) 计算综合影响矩阵T。矩阵N不断自乘可得因素间的间接影响程度,把直接影响和间接影响相加可得到综合影响程度。计算方法如下式:
T = N + N 2 + + N n = n = 1 N n = N e - N - 1
式中:N为规范化直接影响矩阵;e为单位矩阵。
4) 计算各因素影响度与被影响度。因素的影响度是指综合影响矩阵中该因素对应的行和,反映的是该因素对其他因素的综合影响水平;被影响度是指综合影响矩阵中该因素对应的列和,反映的是该因素受其他因素的综合影响水平。计算方法如下式:
D i = j = 1 n t i j ( i = 1,2 n )
F i = j = 1 n t j i ( i = 1,2 n )
式中:Di为因素i的影响度;Fi为因素i的被影响度。
5) 计算各因素中心度和原因度。将因素对应的影响度和被影响度相加得到该因素的中心度,中心度反映的是该因素在系统中与其他因素的联系程度,原因度反映的是该因素在系统中的因果属性,若为正值,表明该因素偏向于影响其他因素,被判定为原因型因素;若为负值,则表明该因素偏向于受到其他因素的影响,被判定为结果型因素。计算方法如下式:
Q i = D i + F i ( i = 1,2 n )
E i = D i - F i ( i = 1,2 n )
式中:Qi为因素i的中心度;Ei为因素i的原因度。
通过重要度分析模型计算得到水电工程物体打击事故致因的影响度V、被影响度Y、中心度U、原因度J、中心度排名q和因素属性b,具体结果见表5
表5可知:水电工程物体打击事故致因包括C1C4C5C6C7C9C10C12C14等9个原因因素。其中,C9C12C14的原因度>1且排名前3,充分说明C9C12C14在水电工程施工中更易导致其他致因发生从而引发物体打击事故,是事故发生的根本原因,必须引起管理人员的高度注意。其余6项原因因素原因度虽低,但主要涉及施工现场的安全管理与施工环境等重要领域,说明水电工程中完善的安全管理体系和良好的施工环境是确保施工安全的必要前提。
结果因素包括C2C3C8C11C13等5个因素,因素原因度越小表明该因素越易受其他因素影响,其中,C8为物体打击事故发生创造了主观环境;C11为潜在致灾体危害能量释放埋下安全隐患(如边坡滚石伤人),而C2极大加深了作业人员在危害能量威胁下的伤亡程度;C3为致灾体(施工机具)作用于承灾体(作业人员)提供了客观的前提条件;C13是导致人的不安全行为的重要条件之一。以上5个因素与事故发生直接相关,是水电工程物体打击事故的近端致因因素,在事故预防中应成为首要控制对象。
事故致因原因-中心度如图4所示。由图4可知:致因C11C8C3C2的中心度在事故系统中排前4,对水电工程物体打击事故系统起到重要影响作用,因此,在施工过程中要不断加强安全教育培训,进而在思想上提高工人的安全生产意识;提高安全防护措施,如穿戴智能感应设备实时监控工人状态;认真落实或完善各项规章管理制度,加强监管人员的责任意识,把安全监督检查工作落到实处,从人-机-管3方面切断事故的致因路径。
致因C1C5C6C9C12C13的中心度均介于1.5~2.6,对事故发生也起到一定的促进作用,因此,在水电工程实际工作中应该适当加强对失职人员的惩处措施,提高现场管理人员的责任意识,调整施工节奏,避免施工交叉混乱,严格落实技术交底工作,加强隐患细节的排查力度,以及增加水电施工人员的技能培训。通过设置合理的奖惩制度提高水电人员的工作热情,降低事故人因风险,借助事故系统的反馈调节机制降低事故发生概率。
其余事故致因的中心度较低,但在施工作业中仍不可忽视,它们彼此影响、相互作用、共同促进事故发生,在事故防控阶段要全方位、多角度综合考虑致因因素,抓住重要的少数,把握次要的多数,从源头遏制事故的发展,尽量减少不必要的损失,确保水电施工安全进行。
1) 采用文本挖掘技术,在所搜集的108份水电工程物体打击事故报告基础之上,结合相关文献归纳总结出14项物体打击事故致因,提高了事故致因的准确性。
2) 采用Apriori算法挖掘水电工程物体打击事故致因间的171条强关联规则,结合指标分析可知:安全监督检查不到位、安全防护措施不完备、安全教育培训不到位3项致因间关联性较强,并通过现实致因过程对相应强关联规则加以验证,为证明事故致因间的关联耦合关系提供重要依据。
3) 基于DEMATEL方法获得各致因因素的影响度、被影响度、中心度和原因度,并分析事故致因重要度,从原因-结果层面揭示了事故致因的诱发机制和事故发生特征,并提出针对性的事故预防措施。
4) 文中未考虑水电工程物体打击事故致因间的路径传递,后续可进一步研究各致因因素的层级影响路径,挖掘事故致因的深层影响机制。
  • 国家自然科学基金资助(72204141)
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doi: 10.16265/j.cnki.issn1003-3033.2024.04.1452
  • 接收时间:2023-10-14
  • 首发时间:2025-07-09
  • 出版时间:2024-04-28
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  • 收稿日期:2023-10-14
  • 修回日期:2024-01-18
基金
国家自然科学基金资助(72204141)
作者信息
    1 三峡大学 水电工程施工与管理湖北省重点实验室,湖北 宜昌 443002
    2 三峡大学 水利与环境学院,湖北 宜昌 443002

通讯作者:

**邵波(1990—),男,湖北孝感人,博士,副教授,主要从事安全管理、风险管理等方面的研究。E-mail:
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2种不同金属材料的力学参数

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Number of
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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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