Article(id=1318974656007230161, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318974603884606018, articleNumber=null, orderNo=null, doi=10.14062/j.issn.0454-5648.20220973, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1668096000000, receivedDateStr=2022-11-11, revisedDate=1670601600000, revisedDateStr=2022-12-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1792397663355, onlineDateStr=2026-10-19, pubDate=1683216000000, pubDateStr=2023-05-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792397663355, onlineIssueDateStr=2026-10-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792397663355, creator=13701087609, updateTime=1792397663355, updator=13701087609, issue=Issue{id=1318974603884606018, tenantId=1146029695717560320, journalId=1263187385517883426, year='2023', volume='51', issue='8', pageStart='1883', pageEnd='2118', issueExtLink='null', onlineDate='null', pubDate='1692460800000', pubDateStr='2023-08-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792397650929, creator='13701087609', updateTime=1792399225992, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1318981210261541260, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318974603884606018, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1318981210261541261, tenantId=1146029695717560320, journalId=1263187385517883426, issueId=1318974603884606018, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2062, endPage=2073, ext={EN=ArticleExt(id=1318974656204362450, articleId=1318974656007230161, tenantId=1146029695717560320, journalId=1263187385517883426, language=EN, title=Development on Machine Learning for Durability Prediction of Concrete Materials, columnId=1263922772401635899, journalTitle=Journal of the Chinese Ceramic Society, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

The durability evaluation of concrete materials based on the related experiments has a low economic efficiency ratio. The prediction accuracy of the conventional empirical formula for durability is restricted and the proportions of concrete mix cannot be calculated according to the performance. It is thus necessary to develop a novel and efficient material quality control and performance prediction tool. In this review, the process of building machine learning models was stated. The basic working flow and advantages of common algorithms, as well as the durability index prediction algorithms based on machine learning were summarized. Its application effects and development direction were discussed. This review can provide a basis for the in-depth development and application of machine learning technology in the field of concrete.

, authors=Xiao LIU^{1, 2}, Simai WANG^{1, 2}, Lei LU^{1, 2}, Meizhu CHEN³, Yue ZHAI^{1, 2}, Suping CUI^{1, 2}, authorsList=Xiao LIU, Simai WANG, Lei LU, Meizhu CHEN, Yue ZHAI, Suping CUI, authorCompany=null, correspAuthors=null, authorNote=, 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=1318974658033078996, articleId=1318974656007230161, tenantId=1146029695717560320, journalId=1263187385517883426, language=CN, title=机器学习预测混凝土材料耐久性的研究进展, columnId=1263922774356181565, journalTitle=硅酸盐学报, columnName=综合评述, runingTitle=null, highlight=null, articleAbstract=

基于实验研究的混凝土材料耐久性评价周期长、成本高、效率低，传统经验公式对耐久性的预测精度有限且无法根据性能反推混凝土的配合比设计，因此开发新型高效的材料质量控制和性能预测工具已是当务之急。通过梳理建立机器学习模型的流程，总结常用算法的基本工作原理和优势，归纳基于机器学习的各耐久性指标预测算法，探讨其应用效果和发展方向，为机器学习技术在混凝土领域的深度开发和应用提供了依据。

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LIU Xiao (1983-), male, Ph.D., Professor. E-mail: liux@bjut.edu.cn

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LIU Xiao (1983-), male, Ph.D., Professor. E-mail: liux@bjut.edu.cn

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刘晓（1983—），男，博士，教授。

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刘晓（1983—），男，博士，教授。

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