Article(id=1223185966500794866, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220711, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1646928000000, receivedDateStr=2022-03-11, revisedDate=1650470400000, revisedDateStr=2022-04-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1769559860030, onlineDateStr=2026-01-28, pubDate=1679673600000, pubDateStr=2023-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769559860030, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769559860030, creator=13701087609, updateTime=1769559860030, updator=13701087609, issue=Issue{id=1223185958363841064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='3', pageStart='1', pageEnd='220', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769559858149, creator=13701087609, updateTime=1769561242661, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223191765415477785, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223191765415477786, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223185958363841064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=144, endPage=148, ext={EN=ArticleExt(id=1223185970372137700, articleId=1223185966500794866, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Strength Prediction Model and Ratio Optimization of Concrete Damaged by Early Freezing, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

In view of the problem that hydraulic concrete buildings in cold regions are vulnerable to early freezing injury during pouring construction and may affect the later healthy service, in order to study the strength damage law of early frozen concrete and optimize its ratio, Box-Behnken (RSM-BBD) response surface method was used to optimize the experimental design. The RSM response surface model was established by taking water binder ratio, fly ash content and air entraining agent content as variables. A GA-BPNN strength prediction model was constructed to predict the strength of early-frozen concrete accurately. Compared with the RSM model, the results show that the GA-BPNN has more accurate prediction performance and can optimize proportion design more efficiently. The goodness of fit R² and average relative error e_MRE by the GA-BPNN strength prediction model are 0.998 5 and 2.13%, respectively. The relative error between the predicted value of the optimal strength ratio and the experimental value is about 1%. The application of GA-BPNN strength prediction model can realize the efficient optimization of concrete freezing strength and its ratio.

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针对严寒地区水工混凝土建筑物在浇筑施工时易受早期冻害，并可能影响后期健康服役的问题，为研究早冻混凝土强度损伤规律并对其配比进行优化，利用RSM响应面法Box-Behnken（RSM-BBD）优化试验设计，并建立以水胶比、粉煤灰掺量、引气剂掺量为变量的RSM响应面模型；同时，构建一种GA-BPNN强度预测模型，实现早冻混凝土强度的精确预测。结果表明，与RSM模型相比，GA-BPNN模型有更精确的预测性能，且能更高效地优化配比设计；GA-BPNN强度预测模型的拟合优度R²和平均相对误差e_MRE分别为0.998 5、2.13%，最优配比强度预测值与试验值的相对误差约为1%，应用该模型可实现混凝土受冻强度推演及其配比的高效优化。

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徐存东（1972-），男，博士、教授、博导，研究方向为水工结构优化设计和耐久性，E-mail：xcundong@126.com

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徐存东（1972-），男，博士、教授、博导，研究方向为水工结构优化设计和耐久性，E-mail：xcundong@126.com

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