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Article(id=1148106699208126808, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106697601704181, articleNumber=1003-3033(2025)01-0032-08, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.01.0396, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724083200000, receivedDateStr=2024-08-20, revisedDate=1729785600000, revisedDateStr=2024-10-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1751659567882, onlineDateStr=2025-07-05, pubDate=1737993600000, pubDateStr=2025-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751659567882, onlineIssueDateStr=2025-07-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751659567882, creator=13701087609, updateTime=1751659567882, updator=13701087609, issue=Issue{id=1148106697601704181, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='1', 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=1751659567499, creator=13701087609, updateTime=1757401533944, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1172190250475573883, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106697601704181, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1172190250475573884, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106697601704181, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=32, endPage=39, ext={EN=ArticleExt(id=1149757470903546663, articleId=1148106699208126808, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Quantitative classification and evolution analysis of construction safety climate, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

, correspAuthors=Zhen TIAN, 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=Tian LEI, Zhen TIAN), CN=ArticleExt(id=1148106704430035449, articleId=1148106699208126808, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=建筑施工安全氛围的量化分级及演化分析, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为探究建筑施工安全氛围的演化特征及其提升的最佳联合策略,借助莫比乌斯环结构,从建筑工人对安全的认知、行为及其所处环境3个维度,构建基于认知-行为-环境的建筑施工安全氛围三因子结构模型;根据建筑施工安全氛围三维空间结构模型划分等级标准,利用动态贝叶斯网络(DBN)研究建筑施工安全氛围随时间的变化情况。结果表明: 就影响因素而言,安全激励对建筑施工安全氛围及其演化的影响程度最大;就维度而言,行为维度的影响程度最大。提升建筑施工安全氛围的最佳联合策略为:依次加强对安全激励、安全督查、工人安全响应、安全意识以及工人间学习与交流这5个影响因素的管控。

, correspAuthors=田震, authorNote=null, correspAuthorsNote=
**田震(1976—),男,湖北浠水人,博士,副教授,主要从事化工过程安全、事故调查与分析技术、事故应急技术与管理等方面的研究。E-mail: 。
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雷恬 (1997—),女,江西丰城人,硕士,主要从事安全管理、安全文化等方面的工作。E-mail:。

田震 副教授

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雷恬 (1997—),女,江西丰城人,硕士,主要从事安全管理、安全文化等方面的工作。E-mail:。

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雷恬 (1997—),女,江西丰城人,硕士,主要从事安全管理、安全文化等方面的工作。E-mail:。

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田震 副教授

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田震 副教授

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American Journal of Epidemiology, 1998, 148(11): 1127-1132., articleTitle=Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic lest, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1165721903139398446, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, xref=1, ext=[AuthorCompanyExt(id=1165721903147787055, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, companyId=1165721903139398446, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 Shenzhen Gas Corporation Ltd., Shenzhen Guangdong 518040, China), AuthorCompanyExt(id=1165721903151981360, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, companyId=1165721903139398446, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 深圳燃气集团股份有限公司,广东 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articleId=1148106699208126808, language=EN, label=Fig.1, caption=Three-factor structure model of construction safety climate, figureFileSmall=Mjtij91bpEjOnBIZj4KKQA==, figureFileBig=MK+0bVKd0601osx6TLOwgw==, tableContent=null), ArticleFig(id=1165721904297026378, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=图1, caption=建筑施工安全氛围三因子结构模型, figureFileSmall=Mjtij91bpEjOnBIZj4KKQA==, figureFileBig=MK+0bVKd0601osx6TLOwgw==, tableContent=null), ArticleFig(id=1165721904343163723, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Fig.2, caption=Three-dimensional structure of safety climate, figureFileSmall=JcLtu8vgni8GaGsd/+2JZw==, figureFileBig=6q6V+XV/qt6zlsNtmoD7Dg==, tableContent=null), ArticleFig(id=1165721904385106764, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=图2, caption=安全氛围三维空间结构, 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label=Fig.6, caption=Influence of dimensions on safety climate, figureFileSmall=ei+8A5T9pSl9T/+OXPrM9w==, figureFileBig=G+tDrKF/41wP4XdO0UNFjg==, tableContent=null), ArticleFig(id=1165721904863257428, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=图6, caption=维度对安全氛围的影响, figureFileSmall=ei+8A5T9pSl9T/+OXPrM9w==, figureFileBig=G+tDrKF/41wP4XdO0UNFjg==, tableContent=null), ArticleFig(id=1165721904909394773, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Fig.7, caption=Joint enhancement strategy for safety climate, figureFileSmall=jjExPzZgw89RvQiXtXttWA==, figureFileBig=Pj08h3fmBXbnpAsudhDYaQ==, tableContent=null), ArticleFig(id=1165721904947143510, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=图7, caption=安全氛围的联合提升策略, figureFileSmall=jjExPzZgw89RvQiXtXttWA==, figureFileBig=Pj08h3fmBXbnpAsudhDYaQ==, tableContent=null), ArticleFig(id=1165721904993280855, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Table 1, caption=

Dimensions and influencing factors

, figureFileSmall=null, figureFileBig=null, tableContent=
维度 影响因素
认知 安全思想和理念(Safety Thoughts
and Ideas, STI)
安全意识(Safety Conscious, SCS)
安全知识(Safety Knowledge, SKL)
环境 自然环境(Natural Environment, NE)
施工现场环境(Construction Site
Environment, SE)
人文环境(Humanistic Environment, HE)
行为 安全管理承诺(Safety Management
Commitment, SMC)
安全激励(Safety Incentive, SIC)
安全督查(Safety Supervision, SSV)
安全教育和培训(Safety Education and
Training, SET)
工人安全响应(Worker Safety Response, WSR)
工人间学习与交流(Workers' Learning and
Communication, WLC)
), ArticleFig(id=1165721905035223896, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=表1, caption=

维度及影响因素

, figureFileSmall=null, figureFileBig=null, tableContent=
维度 影响因素
认知 安全思想和理念(Safety Thoughts
and Ideas, STI)
安全意识(Safety Conscious, SCS)
安全知识(Safety Knowledge, SKL)
环境 自然环境(Natural Environment, NE)
施工现场环境(Construction Site
Environment, SE)
人文环境(Humanistic Environment, HE)
行为 安全管理承诺(Safety Management
Commitment, SMC)
安全激励(Safety Incentive, SIC)
安全督查(Safety Supervision, SSV)
安全教育和培训(Safety Education and
Training, SET)
工人安全响应(Worker Safety Response, WSR)
工人间学习与交流(Workers' Learning and
Communication, WLC)
), ArticleFig(id=1165721905089749849, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Table 2, caption=

Results of questionnaire reliability and validity test

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 项数 Cronbach's alpha CR AVE
STI 3 0.863 0.864 0.680
SCS 5 0.815 0.849 0.531
SKL 3 0.763 0.764 0.520
SMC 3 0.931 0.936 0.830
SIC 3 0.815 0.817 0.600
SSV 3 0.916 0.916 0.785
SET 3 0.768 0.769 0.526
WSR 3 0.803 0.812 0.595
WLC 3 0.705 0.781 0.543
NE 3 0.752 0.785 0.550
SE 3 0.824 0.821 0.605
HE 3 0.907 0.908 0.768
), ArticleFig(id=1165721905148470106, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=表2, caption=

问卷信度、效度检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量 项数 Cronbach's alpha CR AVE
STI 3 0.863 0.864 0.680
SCS 5 0.815 0.849 0.531
SKL 3 0.763 0.764 0.520
SMC 3 0.931 0.936 0.830
SIC 3 0.815 0.817 0.600
SSV 3 0.916 0.916 0.785
SET 3 0.768 0.769 0.526
WSR 3 0.803 0.812 0.595
WLC 3 0.705 0.781 0.543
NE 3 0.752 0.785 0.550
SE 3 0.824 0.821 0.605
HE 3 0.907 0.908 0.768
), ArticleFig(id=1165721905211384667, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Table 3, caption=

Classification standard of dimensions and influencing factors

, figureFileSmall=null, figureFileBig=null, tableContent=
等级 范围 描述
Weak [1, 3) 不符合现状,十分欠缺
Medium [3, 4) 一般符合现状
Strong [4, 5] 非常符合现状,总体令人满意
), ArticleFig(id=1165721905249133404, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=表3, caption=

维度及影响因素分级标准

, figureFileSmall=null, figureFileBig=null, tableContent=
等级 范围 描述
Weak [1, 3) 不符合现状,十分欠缺
Medium [3, 4) 一般符合现状
Strong [4, 5] 非常符合现状,总体令人满意
), ArticleFig(id=1165721905320436573, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Table 4, caption=

Safety climate strength classification standard

, figureFileSmall=null, figureFileBig=null, tableContent=
等级 x、y、z取值范围 R2取值范围 描述
R1 [1, 2) 3≤ R2 <12 弱
R2 [2, 3) 12≤ R2 <27 中等
R3 [3, 4) 27≤ R2 <48 良好
R4 [4, 5] 48≤ R2 ≤75 强
), ArticleFig(id=1165721905366573918, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=CN, label=表4, caption=

安全氛围强度分级标准

, figureFileSmall=null, figureFileBig=null, tableContent=
等级 x、y、z取值范围 R2取值范围 描述
R1 [1, 2) 3≤ R2 <12 弱
R2 [2, 3) 12≤ R2 <27 中等
R3 [3, 4) 27≤ R2 <48 良好
R4 [4, 5] 48≤ R2 ≤75 强
), ArticleFig(id=1165721905412711263, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1148106699208126808, language=EN, label=Table 5, caption=

Influence of influencing factors on evolution of safety climate

, figureFileSmall=null, figureFileBig=null, tableContent=
变
量		 安全氛围处于R4等级
的概率		 差值 排
名
T=1 T=12
SCS 0.212 957 520 0.349 560 859 0.136 603 339 4
STI 0.203 120 337 0.334 687 890 0.131 567 554 6
SKL 0.243 165 453 0.324 387 491 0.081 222 038 12
SE 0.248 495 839 0.365 837 177 0.117 341 338 9
NE 0.226 459 047 0.356 309 466 0.129 850 419 8
HE 0.268 948 384 0.356 384 091 0.087 435 707 11
SMC 0.194 261 337 0.324 387 491 0.130 126 155 7
SET 0.210 530 704 0.324 387 491 0.113 856 787 10
SIC 0.191 799 130 0.344 000 426 0.152 201 296 1
WLC 0.218 688 844 0.351 393 905 0.132 705 061 5
SSV 0.202 825 719 0.354 167 239 0.151 341 521 2
WSR 0.198 796 673 0.344 180 214 0.145 383 541 3
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影响因素对安全氛围演化的影响

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变
量		 安全氛围处于R4等级
的概率		 差值 排
名
T=1 T=12
SCS 0.212 957 520 0.349 560 859 0.136 603 339 4
STI 0.203 120 337 0.334 687 890 0.131 567 554 6
SKL 0.243 165 453 0.324 387 491 0.081 222 038 12
SE 0.248 495 839 0.365 837 177 0.117 341 338 9
NE 0.226 459 047 0.356 309 466 0.129 850 419 8
HE 0.268 948 384 0.356 384 091 0.087 435 707 11
SMC 0.194 261 337 0.324 387 491 0.130 126 155 7
SET 0.210 530 704 0.324 387 491 0.113 856 787 10
SIC 0.191 799 130 0.344 000 426 0.152 201 296 1
WLC 0.218 688 844 0.351 393 905 0.132 705 061 5
SSV 0.202 825 719 0.354 167 239 0.151 341 521 2
WSR 0.198 796 673 0.344 180 214 0.145 383 541 3
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建筑施工安全氛围的量化分级及演化分析
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雷恬 1 , 田震 2, **
中国安全科学学报 | 安全社会科学与安全管理 2025,35(1): 32-39
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中国安全科学学报 | 安全社会科学与安全管理 2025, 35(1): 32-39
建筑施工安全氛围的量化分级及演化分析
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雷恬1 , 田震2, **
作者信息
  • 1 深圳燃气集团股份有限公司,广东 深圳 518040
  • 2 华南理工大学 机械与汽车工程学院,广东 广州 510641

    • 雷恬 (1997—),女,江西丰城人,硕士,主要从事安全管理、安全文化等方面的工作。E-mail:。

    田震 副教授

通讯作者:

**田震(1976—),男,湖北浠水人,博士,副教授,主要从事化工过程安全、事故调查与分析技术、事故应急技术与管理等方面的研究。E-mail: 。
Quantitative classification and evolution analysis of construction safety climate
Tian LEI1 , Zhen TIAN2, **
Affiliations
  • 1 Shenzhen Gas Corporation Ltd., Shenzhen Guangdong 518040, China
  • 2 School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou Guangdong 510641, China
出版时间: 2025-01-28 doi: 10.16265/j.cnki.issn1003-3033.2025.01.0396
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摘要
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为探究建筑施工安全氛围的演化特征及其提升的最佳联合策略,借助莫比乌斯环结构,从建筑工人对安全的认知、行为及其所处环境3个维度,构建基于认知-行为-环境的建筑施工安全氛围三因子结构模型;根据建筑施工安全氛围三维空间结构模型划分等级标准,利用动态贝叶斯网络(DBN)研究建筑施工安全氛围随时间的变化情况。结果表明: 就影响因素而言,安全激励对建筑施工安全氛围及其演化的影响程度最大;就维度而言,行为维度的影响程度最大。提升建筑施工安全氛围的最佳联合策略为:依次加强对安全激励、安全督查、工人安全响应、安全意识以及工人间学习与交流这5个影响因素的管控。

建筑施工  /  安全氛围  /  量化分级  /  演化分析  /  动态贝叶斯网络(DBN)

In order to explore the evolution mechanism and the best joint strategy for improvement of construction safety climate, with the help of the Mobius ring structure, a three-factor structure model of construction safety climate based on cognition-behavior-environment was constructed from three dimensions of construction workers' cognition, behavior and environment. According to the classification standard of the three-dimensional spatial structure model of construction safety climate, the grading standard of safety climate was divided. The DBN was used to study the changes of the construction safety climate with time. The results show that in terms of influencing factors, safety incentives have the greatest impact on enterprise safety climate and its evolution. In terms of dimensions, the behavioral dimension has the greatest impact. The best joint strategy to improve the construction safety climate is to strengthen the control of safety incentives, safety supervision, workers' safety response, safety consciousness and workers' learning and communication in turn.

construction  /  safety climate  /  quantitative classification  /  evolution analysis  /  dynamic Bayesian network (DBN)
雷恬, 田震. 建筑施工安全氛围的量化分级及演化分析. 中国安全科学学报, 2025 , 35 (1) : 32 -39 . DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0396
Tian LEI, Zhen TIAN. Quantitative classification and evolution analysis of construction safety climate[J]. China Safety Science Journal, 2025 , 35 (1) : 32 -39 . DOI: 10.16265/j.cnki.issn1003-3033.2025.01.0396
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0 引言
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建筑施工是工程量大、施工周期长、涉及方面广且结构复杂的系统性工程,这些特点在一定程度上决定了各种不安全因素隐藏在整个施工过程中,成为安全隐患,在一些外部条件干预下引发施工安全事故。而良好的安全氛围能够有效促使施工现场的工作人员主动遵守安全操作规程,抑制侥幸心理及不安全行为产生,提升工人安全意识,更有效地避免施工现场安全事故发生。
安全氛围最早由以色列学者ZOHAR于1980年提出[1],而且ZOHAR[2]还认为,安全氛围是生产工人对组织安全生产方面的共同看法,积极的安全氛围可降低事故率。NEAL等[3]指出从长远角度看,要降低企业事故率,提升安全氛围比惩罚后再培训员工更高效。目前对安全氛围的研究主要来自4个方面:①安全氛围维度的研究。张力等[4]提出了影响核电厂建造施工班组安全氛围的5个维度,分别为项目部安全职责、班组内沟通、班组成员安全态度、施工现场管理和施工作业环境。LI Qiming等[5]从企业管理角度提出了工人安全自我感知、工人安全参与、同事互动、安全环境、安全管理参与和安全人员支持6个安全氛围维度。②安全氛围与安全行为、安全结果、安全绩效等的相互关系研究。冯亚娟等[6]采用结构方程模型阐明了安全氛围和不安全行为呈负相关关系。杨雪等[7]采用元分析法研究得出相较安全氛围,安全绩效对安全结果有更强的影响。王丹等[8]利用多层次线性模型研究了群体层的安全氛围与建筑工人安全行为之间呈现正相关。③安全氛围的测评。HAN等[9]系统论述了建筑行业采用文献调查、问卷调查和问卷分析3种方法测量了安全氛围。HUANG等[10]研究了基于项目反映理论的安全氛围测量方法。④安全氛围的影响机制研究。BABY等[11]使用解释结构模型分析数据,发现所有个人因素与安全氛围因素均存在显著相关。MURPHY等[12]指出安全氛围是通过各种组织和工作相关因素的相互作用而产生的,这种相互作用是复杂的、非线性的。综上所述,国内外学者对安全氛围多采用结构方程模型、元分析法、解释结构模型等评价方法进行静态分析,而安全氛围被定义为人对安全状态的感知,是随时间动态变化的,当前缺乏对企业安全氛围动态性演化分析的研究。
近年来,动态贝叶斯网络(Dynamic Bayesian Network, DBN)被广泛应用于动态系统建模和状态预测等方面,推理具有动态不确定性的问题,如刘一鹏等[13]基于DBN从人、机、环、管4个角度构建了水下地铁的施工安全风险动态演化模型,预测了风险的动态演化路径;马舒琪等[14]采用DBN构建了古建筑群火灾情景推演路径,便于古建筑群火灾的精准应急管理和风险防控;王洪涛等[15]建立了DBN预测电价区间以规避价格风险。以上研究表明:针对具有不确定性的影响因素,DBN不仅能基于网络模型研究各影响因素之间的演化路径,还能预测其随时间推移的动态变化过程。
鉴于此,笔者拟从建筑工人对安全的认知、行为及其所处环境3个维度构建基于认知-行为-环境的建筑施工安全氛围三因子结构模型,探索建筑施工安全氛围对应维度下的影响因素,并分别进行定量分级,再借助DBN模拟预测建筑施工安全氛围随时间的动态变化过程,通过演化分析,研究其最佳提升策略。
1 建筑施工安全氛围三因子结构模型
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软件-硬件-环境-人员(Software-Hardware-Environment-Liveware, SHEL)模型指出人与系统中的软件、硬件、环境以及其他人的相互关系构成了SHEL模型的4个界面,即人-软件(L-S)、人-硬件(L-H)、人-环境(L-E)、人-人(L-L),该模型认为人是系统中最活跃的一个组成部分[16]。而建筑施工安全氛围与SHEL模型类似,同样强调人的核心性和主体性地位,且L-H界面和L-L界面可以共同描述为人对周围事物所产生的行为,L-S界面则表达了人对人对系统中软件要素的认知,因此,结合建筑施工安全生产特点,将建筑施工安全氛围划分为施工人员对安全的认知、施工人员的行为以及环境3个维度,施工安全则是这3个维度协调运转的状态与结果。
在确定维度基础上,通过大量文献阅读和访谈安全管理领域专家及学者,最终确定建筑施工安全氛围各维度的影响因素。安全思想和理念、安全意识和安全知识是认知维度的主要影响因素,环境维度的影响因素划分为自然环境、施工现场环境和人文环境,行为维度由安全管理承诺、安全激励、安全督查、工人安全响应、安全教育培训及工人间学习与交流6个影响因素共同决定,具体见表1。
为更好地描述建筑施工安全氛围的内部影响机制,借助莫比乌斯环描述建筑施工安全氛围结构,构建基于认知-行为-环境的建筑施工安全氛围三因子结构模型,如图1所示。一方面,建筑施工人员所处的环境在一定程度上会对他们是否行为安全产生直接影响,另一方面,环境通过影响建筑施工人员对安全的认知形成,进而由认知决定了他们的行为是否安全。因此,认知、行为以及环境三者之间相互影响,共同促进,营造出浓厚的安全氛围。
2 建筑施工安全氛围的量化分级
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2.1 建筑施工安全氛围调查分析
采用LIKERT五级量表对来自广东的多家建筑施工领域相关企业的项目管理人员、现场管理人员、现场施工人员以及安全管理领域专家及学者进行问卷调查,采取线上专业问卷调查网站发布和线下现场调研2种形式进行问卷发放,共回收问卷378份,最终确定343份有效问卷,问卷有效率达到90.7%。问卷的数据分析结果见表2。
1) 信度分析。利用SPSS25.0软件检验调查问卷的信度,问卷总体的Cronbach's alpha系数为0.820,且各影响因素的Cronbach's alpha均在0.705~0.931间,表明该问卷有较高的可信度。
2) 效度分析。采用因子分析法得到KMO检验和Bartlett球形检验的2个指标值,问卷的KMO检验的系数为0.782,符合标准;Bartlett球形检验显著p<0.001,表明该问卷适用于因子分析。通过各因子标准负荷计算得出安全氛围的各个影响因素的CR均大于0.7,AVE均大于0.5,说明该问卷具有较好的内部一致性和收敛效度。
2.2 建筑施工安全氛围等级划分
依据建筑施工安全氛围维度及其影响因素,定义建筑施工安全氛围DBN模型的变量。由于问卷调查采用的是五级量表,通过预处理回收问卷数据,发现各维度影响因素的题项分值中“1”这一选项占比极低,为体现等级的有效性,采用行业实际情况调研、专家访谈等方式,在等级划分时将取值范围[1, 2)和[2, 3)合并为[1, 3),由此确定建筑施工安全氛围3个维度和12个影响因素的3个等级,Weak、Medium以及Strong,维度取其影响因素得分的平均值,具体划分见表3。
为量化分级建筑施工安全氛围,基于认知—行为—环境的建筑施工安全氛围三因子结构模型,将3个维度分别作为X轴、Y轴、Z轴,构成建筑施工安全氛围的三维空间结构,如图2所示。
原点O为调查对象的初始位置,点A由3个维度的分值共同决定,表示所营造安全氛围的最大极限点。其中3个维度的分值分别取调查问卷中各自影响因素题项得分的平均值,点A到原点O的直线距离OA为球体半径R,安全氛围的辐射强度为R2,计算步骤如下:
x = 1 5 ∑ i = 1 5 ( S C S ) i + 1 6 ∑ i = 1 3 [ ( S T I ) i + ( S K L ) i ]
y = 1 18 ∑ i = 1 3 [ ( S M C ) i + ( S I C ) i + ( S S V ) i + ( S E T ) i + ( W S R ) i + ( W L C ) i ]
z = 1 9 ∑ i = 1 3 [ ( N E ) i + ( S E ) i + ( H E ) i ]
R 2 = x 2 + y 2 + z 2
式中:x为认知维度;y为行为维度;z为环境维度;i为影响因素题项数。
基于此,参照安全风险分级标准,将建筑施工安全氛围的强度划分为4个等级:R1、R2、R3和R4,从低到高依次描述为弱、中等、良好和强,分级如图3所示,分级标准见表4。
3 基于DBN的建筑施工安全氛围演化分析
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3.1 安全氛围演化的DBN模型
DBN是建立在静态贝叶斯网络和马尔可夫模型上的图结构,是对初始网络的一种定时扩展,由初始网络和传输网络组成,每个时间片段对应1个静态贝叶斯网络[17-18]。使用GeNIe3.0学术版构建和测试企业安全氛围演化模型,采用70%的数据(样本量n=240)用于参数学习,剩余30%数据(样本量n=103)用于预测模型的验证。首先通过结构学习算法获得贝叶斯网络结构,再根据专家组(由1名某市安全生产协会专家组成员、2名某高校安全领域教授及3名企业安全部门经理组成,均拥有7年以上安全管理经验,涉及建筑施工安全、化工过程安全、消防安全等领域)意见,修改完善现有贝叶斯网络结构。最后,在此基础上设置12个时间片T,每个时间片代表1个月,形成DBN结构,如图4所示。
3.2 安全氛围动态演化结果分析
在建筑施工安全氛围演化DBN模型基础上进行参数学习,将各观测节点的时序数据作为证据信息输入到DBN中进行演化分析,预测建筑施工安全氛围随时间变化的情况。各观测点的时序数据根据多家建筑施工企业过去1年内实际情况及施工管理人员访谈获取,预测结果如图5所示。结果表明:在接下来的1年时间内,建筑施工安全氛围处于R1和R3等级的概率波动幅度不大,但整体有所降低,处于R2等级的概率呈现较为明显的下降趋势,到年末降至最低值6.68%,处于R4等级的概率一直稳步上升,到年末达到最高值32.44%。
遵循上述预测推理过程,随着建筑施工安全氛围的演化,网络中各个节点的观测数据在不停地发生变化,通过将新的时序观测数据输入到DBN模型中,可实时预测未来一段时间安全氛围的变化[19]。预测精度是评估DBN模型预测性能的最常用工具。为验证所构建的安全氛围演化预测的DBN模型性能,选取剩余30%的时间窗口数据作为测试集,以建筑施工安全氛围作为节点,采用K-fold交叉验证法(设置K=10)验证分析。被试工作特征(Receiver Operating Characteristic, ROC)曲线显示了预测的准确性,ROC曲线下的面积(Area Under Curve, AUC)是衡量模型优劣的一种评价指标。AUC大于0.9被认为是优秀的预测准确率,0.8~0.9表示非常好的预测准确率,0.7~0.8表示良好的预测准确率,0.6~0.7表示一般的预测准确率,如果低于0.6则预测准确率较差[20]。企业安全氛围DBN演化模型的总体精度(Accuracy, ACC)为0.812 5,说明预测准确率非常好。
由图2的安全氛围三维空间结构可知:建筑施工安全氛围是通过3个维度之间相互作用而形成的,为探索3个维度对安全氛围等级达到R4的影响程度,对图4的DBN模型分别设置了7种不同条件(x、y、z单一或其组合等级均为Strong),维度对安全氛围的影响如图6所示。结果表明:单一维度变化中行为维度明显对预测建筑施工安全氛围的影响程度更大,使其年末处于R4等级的概率高达65.84%,环境维度次之,认知维度对建筑施工安全氛围预测的影响最小;当3个维度均为Strong时,安全氛围处于R4等级的概率为80.56%。综上所述,对预测建筑施工安全氛围等级处于R4的影响程度为:行为维度>环境维度>认知维度,表明就长期的影响情况而言,规范建筑施工人员的行为对提升建筑施工安全氛围更有效。
3.3 安全氛围的最佳联合提升策略
基于建筑施工安全氛围DBN演化模型,各影响因素对建筑施工安全氛围的影响见表5。
由T=1及T=12时建筑施工安全氛围处于R4等级的概率差值排名可知:单一建筑施工安全氛围影响因素状态为Strong时,1年后建筑施工安全氛围处于R4的概率大小排名为:SIC>SSV>WSR>SCS>WLC>STI>SMC>NE>SE>SET>HE>SKL。
根据排名依次改变各影响因素的状态,得出安全氛围的最佳联合提升策略如图7所示。SIC单一因素可使建筑施工安全氛围处于R4的概率提升到34.4%,在后续增加影响因素过程中概率逐渐上升,直至增加安全思想和理念这一影响因素,概率总体较初始呈现着上涨趋势,但涨幅极小,低至1.4%,接着增加安全管理承诺因素,1年后概率保持不变,直至所有影响因素均达到Strong等级,建筑施工安全氛围处于R4的概率提升到72.5%,因此,得出安全氛围的最佳联合提升策略:SIC+SSV+WSR+SCS+WLC,可有效地使建筑施工安全氛围处于R4的概率提升到67.3%。建筑施工企业可基于此结论,在认知维度方面,加大安全投入,潜移默化激发安全意识;在行为维度方面,完善奖惩制度,建立激励机制,强化安全督查,实现人人要安全的大安全格局,加强工人安全响应,落实全员参与方针,重视安全交流,打造良好的学习氛围,从而更加有效地提升企业的安全氛围等级,营造更加浓厚的安全氛围。
4 结论
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1) 基于认知-行为-环境的建筑施工安全氛围三因子结构模型包含施工人员对安全的认知、施工人员的行为以及环境3个维度。根据安全风险等级标准,利用三维空间结构量化分级建筑施工安全氛围强度,从低到高划分为弱、中等、良好和强4个等级。
2) 基于DBN的建筑施工安全氛围演化分析结果表明:就维度而言,对建筑施工安全氛围的影响程度:行为维度>环境维度>认知维度。就影响因素而言,静态情况下,安全意识和安全知识对安全氛围的影响跨度最大,其次是施工现场环境、安全激励、工人间学习与交流等;动态情况下,安全激励对建筑施工安全氛围演化的影响程度最大,其次是安全督查、工人安全响应、安全意识等。
3) 企业安全氛围的最佳联合提升策略:依次加强对安全激励、安全督查、工人安全响应、安全意识以及工人间学习与交流这5个影响因素的管控,可更高效地营造浓厚的安全氛围。
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doi: 10.16265/j.cnki.issn1003-3033.2025.01.0396
  • 接收时间：2024-08-20
  • 首发时间：2025-07-05
  • 出版时间：2025-01-28
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  • 收稿日期：2024-08-20
  • 修回日期：2024-10-25
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    1 深圳燃气集团股份有限公司,广东 深圳 518040
    2 华南理工大学 机械与汽车工程学院,广东 广州 510641

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**田震(1976—),男,湖北浠水人,博士,副教授,主要从事化工过程安全、事故调查与分析技术、事故应急技术与管理等方面的研究。E-mail: 。
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