Article(id=1222925291115241836, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222925278838513745, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20220936, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1649174400000, receivedDateStr=2022-04-06, revisedDate=1653235200000, revisedDateStr=2022-05-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1769497710255, onlineDateStr=2026-01-27, pubDate=1677254400000, pubDateStr=2023-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769497710255, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769497710255, creator=13701087609, updateTime=1769497710255, updator=13701087609, issue=Issue{id=1222925278838513745, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='2', 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=1769497707328, creator=13701087609, updateTime=1769497707328, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=154, endPage=158, ext={EN=ArticleExt(id=1222925291832467856, articleId=1222925291115241836, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Bayesian Stochastic Model for Carbonation Depth of In-service Concrete Structures, columnId=1222925283779400191, journalTitle=Water Resources and Power, columnName=WATER CONSERVANCY AND HYDROPOWER ENGINEERING, runingTitle=null, highlight=null, articleAbstract=

Due to the randomness of concrete carbonation process, the results obtained by the existing carbonation depth prediction models are difficult to characterize the actual carbonation status of in-service concrete structures. Therefore, combining the existing stochastic carbonization model with the actual measurement information of the carbonization depth of in-service concrete structures, a calculation method of a priori distribution hyper-parameters was given when the mean and variance of carbonized population samples were unknown. A Bayesian random model for the carbonation depth of in-service concrete structures was established. Taking the measured concrete carbonation depth of spillway pier and other structures of Yanshan Reservoir as an engineering example, the results show that the Bayesian random model can better represent the actual carbonation condition of in-service concrete structure.

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由于混凝土碳化过程的随机性，现有的碳化深度预测模型难以表征在役混凝土结构的实际碳化状况。为此，将已有碳化随机模型与现役混凝土结构碳化深度的实测信息相结合，提出一种碳化总体样本均值与方差均未知情况下的先验分布超参数的计算方法；并利用贝叶斯理论对碳化速度系数进行更新，研究了针对在役混凝土结构碳化深度的贝叶斯随机模型。以燕山水库溢洪道闸墩等结构的实测混凝土碳化深度为例进行分析，结果表明碳化深度的贝叶斯随机模型能更好地表征在役混凝土结构的实际碳化状况。

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王建有（1977-），男，博士、副教授，研究方向为水工建筑物的安全性检测与评价，E-mail：wangjianyou@zzu.edu.cn

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王建有（1977-），男，博士、副教授，研究方向为水工建筑物的安全性检测与评价，E-mail：wangjianyou@zzu.edu.cn

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