Article(id=1281683223742755026, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.05.0744, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736784000000, receivedDateStr=2025-01-14, revisedDate=1742227200000, revisedDateStr=2025-03-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1783506693063, onlineDateStr=2026-07-08, pubDate=1748361600000, pubDateStr=2025-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783506693063, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783506693063, creator=13701087609, updateTime=1783506693063, updator=13701087609, issue=Issue{id=1281683189131359212, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='5', pageStart='1', pageEnd='268', issueExtLink='null', onlineDate='null', pubDate='1748361600000', pubDateStr='2025-05-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783506684811, creator='13701087609', updateTime=1783589333681, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282029843684504545, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282029843684504546, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281683189131359212, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=47, endPage=55, ext={EN=ArticleExt(id=1281683223952470227, articleId=1281683223742755026, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Intelligent generation of tunnel high ground temperature prevention and control measures based on cloud case-based reasoning, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to solve the problem of ambiguity and limitation of application scope in the establishment of safety prevention and control measures for high-temperature tunnel construction, a method of generating prevention and control measures for high-temperature tunnels based on C-CBR is proposed. Firstly, the index system of the cloud case base is constructed for the tunnel high ground temperature and heat damage problem and combined with the analytic hierarchy process(AHP)-entropy weighting method for the comprehensive weighting calculation. Secondly, the cloud historical case base is constructed based on the three basic characteristic indexes of the cloud model, namely expectation, entropy, and hyperentropy, and the principle of case-based reasoning, and the collaborative filtering algorithm is applied to preliminarily filter the historical cases which are in line with the requirements. And then, the cloud model algorithm which integrates the shape-distance is introduced to achieve the integration between target cases and historical case base. Then, the integrated shape-distance cloud model algorithm is introduced to achieve similarity matching between the target cases and the historical case base, to correct the similarity and update the historical case base to generate the optimal similarity prevention and control measures; finally, the optimal similarity prevention and control measures are fully compared with the actual prevention and control measures in the field. The results show that the corrected similarity between the optimal historical case and the target case generated based on C-CBR is 0.746 0, which is a good match, and the the target case similar prevention and control measures are basically in line with the actual prevention and control measures of the project, which makes the generation method more applicable to different tunnels with high ground temperatures under construction.

, authors=Jingchun WANG, Siao TIAN, authorsList=Jingchun WANG, Siao TIAN, authorCompany=null, correspAuthors=null, 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=1281683224283820245, articleId=1281683223742755026, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于云案例推理的隧道高地温防控措施智能生成, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为解决高地温隧道施工安全防控措施建立过程中存在的模糊性与应用范围的局限性问题，提出一种基于云案例推理（C-CBR）的隧道高地温防控措施生成方法。首先，针对隧道高地温热害问题构建云案例库指标体系，并结合层次分析法（AHP）-熵权法进行综合权重计算；其次，基于云模型期望、熵、超熵3项基本特征指标与案例推理（CBR）原理构建云历史案例库，运用协同过滤算法初步筛选符合要求的历史案例；然后，引入统筹形状-距离的云模型算法实现目标案例与历史案例库间的相似度匹配，修正相似度并更新历史案例库，生成最优相似防控措施；最后，充分对比最优相似防控措施与现场实际防控措施。结果表明：基于C-CBR生成的最优历史案例与目标案例间修正相似度为0.746 0，匹配度较好，目标案例相似防控措施与工程实际防控措施基本符合，生成方法可较好地适用于不同在建高地温隧道。

, authors=王景春教授, 田思奥, authorsList=王景春教授, 田思奥, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=l/NKLtJopp9rq241r5uhPQ==, magXml=sVb5oJd+YA+PzZYnY82Gkw==, pdfUrl=null, pdf=XjAfz+9NZmZIiQEWs36U2Q==, pdfFileSize=2495289, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=zTxjHNtU6JdH0BYnyzNoRQ==, mapNumber=null, fund=null)}, authors=[Author(id=1281683224669696218, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683223742755026, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=wjc36295@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281683224975880413, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683223742755026, authorId=1281683224669696218, language=EN, stringName=Jingchun WANG, firstName=Jingchun, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang Hebei 050043, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281683225042989278, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281683223742755026, authorId=1281683224669696218, language=CN, stringName=王景春教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=石家庄铁道大学 安全工程与应急管理学院，河北 石家庄 050043, bio={"content":"

王景春（1968—），男，河北邢台人，博士，教授，主要从事岩土及地下工程灾害防治等方面的研究。E-mail: wjc36295@163.com。

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王景春（1968—），男，河北邢台人，博士，教授，主要从事岩土及地下工程灾害防治等方面的研究。E-mail: wjc36295@163.com。

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