Article(id=1282034754904101116, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.10.0630, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747411200000, receivedDateStr=2025-05-17, revisedDate=1752940800000, revisedDateStr=2025-07-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783590504623, onlineDateStr=2026-07-09, pubDate=1761580800000, pubDateStr=2025-10-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783590504623, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783590504623, creator=13701087609, updateTime=1783590504623, updator=13701087609, issue=Issue{id=1282034747870253265, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='10', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1761580800000', pubDateStr='2025-10-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783590502946, creator='13701087609', updateTime=1783593819616, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048659068006772, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048659068006773, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282034747870253265, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=75, endPage=81, ext={EN=ArticleExt(id=1282034755138982141, articleId=1282034754904101116, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Construction safety accident prediction model based on GWO-RF, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to reduce the occurrence of building construction safety accidents, association rules were used to reveal the mechanism of accident association, and the optimized RF was fused to predict the occurrence of accidents. First, the causal factors of 388 case reports of construction safety accidents were extracted using 24Model as the theoretical basis. Then, Apriori algorithm was used to excavate the interrelated action paths between the accident causal factors. Finally, hyper-parameters of RF were optimized using GWO algorithm, and the GWO-RF prediction model of construction safety accidents was constructed. And the accident causal factors were the characteristic importance ranking was carried out. The results show that: unsafe behavior, safety ability of organization members, safety management system and safety culture elements constitute a combination of strong correlation conditions. GWO can effectively optimize the hyper-parameters of RF, and prediction accuracy of the optimized GWO-RF model is as high as 93.2%. The characteristic importance ranking shows that: safety education and training have the greatest influence on the prediction of construction safety accidents, with a weighting of 10.5% and a weighting of 10.5%. The importance ranking of features shows that: safety education and training has the greatest influence on the prediction of building construction safety accidents, with a weight of 10.5%. And safety integration management, safety production rules and regulations, and safety production responsibility system are the important factors affecting the prediction of building construction safety accidents, with weights of 7.5%, 7%, and 6%, in that order.

, authors=Dan WANG, Xianglian PAN, authorsList=Dan WANG, Xianglian PAN, authorCompany=null, correspAuthors=Xianglian PAN, 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, fund=null), CN=ArticleExt(id=1282034755369668862, articleId=1282034754904101116, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于GWO-RF的建筑施工安全事故预测模型, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为减少建筑施工安全事故的发生，利用关联规则揭示事故关联机制，并融合优化后的随机森林（RF），预测事故发生情况。首先，以24Model为理论依据，提取388份建筑施工安全事故案例报告的致因因素；然后，采用Apriori算法挖掘事故致因因素之间的相互关联作用路径；最后，利用灰狼优化算法（GWO）优化RF的超参数，构建GWO-RF建筑施工安全事故预测模型，并对事故致因因素进行特征重要性排序。结果表明：不安全行为、组织成员的安全能力、安全管理体系以及安全文化元素构成强相关条件组合；GWO能够有效优化RF的超参数，优化后建立的建筑施工安全事故预测模型（GWO-RF）预测准确率高达93.2%；特征重要性排序显示：安全教育培训对建筑施工安全事故预测的影响最大，权重为10.5%，安全融入管理、安全生产规章制度、安全生产责任制度是影响建筑施工安全事故预测的重要因素，其权重依次为7.5%、7%、6%。

, authors=王丹教授, 潘祥莲, authorsList=王丹教授, 潘祥莲, authorCompany=null, correspAuthors=潘祥莲, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=xPJg3PDNh5K1L9Dur71yKQ==, magXml=hds/Nohlb/QEkVcX18rSPg==, pdfUrl=null, pdf=j4rD56gRHIeEm/C0AoVN6A==, pdfFileSize=4582993, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=iBDYgQTUx4tmANGtuI2p3Q==, mapNumber=null, fund=null)}, authors=[Author(id=1282034757089333507, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034754904101116, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wd-wangdan@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282034757173219589, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034754904101116, authorId=1282034757089333507, language=EN, stringName=Dan WANG, firstName=Dan, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Business Administration, Liaoning Technical University, Huludao Liaoning 125105, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282034757240328454, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034754904101116, authorId=1282034757089333507, language=CN, stringName=王丹教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=辽宁工程技术大学 工商管理学院，辽宁 葫芦岛 125105, bio={"content":"

王丹（1978—），女，辽宁铁岭人，博士，教授，博士生导师，主要从事安全与应急管理、工程可持续发展等方面的研究。E-mail:wd-wangdan@163.com。

"}, bioImg=null, bioContent=

王丹（1978—），女，辽宁铁岭人，博士，教授，博士生导师，主要从事安全与应急管理、工程可持续发展等方面的研究。E-mail:wd-wangdan@163.com。

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