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自然环境条件下混凝土徐变预测模型 被引量:7

Prediction Model of Concrete Creep in Natural Environment
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摘要 为得到合理的自然环境条件下混凝土徐变预测模型,探讨了恒定温度、交变温度、交变温度和相对湿度对既有徐变预测模型的影响;收集了45组控制温、湿度下的混凝土徐变试验结果,分析了温度和相对湿度对混凝土徐变的影响;结合试验结果,拟合了混凝土徐变预测模型公式,并用试验结果进行了验证。结果表明:既有变化温、湿度作用下的徐变预测模型对温度的敏感性有较大差异;交变相对湿度对混凝土徐变的影响小于交变温度;自然环境与试验室标准环境下混凝土徐变系数比值可分为稳定部分和变化部分,稳定部分受平均温度、加载龄期、混凝土强度、试验养护条件等因素影响,变化部分受温度时间历程影响;本文公式可为分析自然环境条件下混凝土徐变提供参考。 To obtain the prediction model of concrete creep under reasonable natural environment,the effects of constant temperature,alternating temperature and relative humidity on the prediction model of existing creep were analyzed.Forty-five groups of concrete creep experimental results under controlled temperature and humidity were collected to analyze the effects of temperature and relative humidity on concrete creep.Then,the formula of concrete creep prediction model was fitted based on the experimental data.The results show that there are significant differences in temperature sensitivity among the creep models when the alternating temperature and relative humidity are considered.The influence of alternating relative humidity on the concrete creep is less than that of alternating temperatures.The ratios of concrete creep coefficients between the test results in natural environment and in laboratory standard environment can be divided into stable part and changed part.The stable part is affected by average temperatures,loading time,concrete strength and curing condition,while the changed part is influenced by alternating temperature time history.The formulas in this paper can provide reference for analyzing the concrete creep in natural environment.
作者 王永宝 贾毅 廖平 赵人达 WANG Yongbao;JIA Yi;LIAO Ping;ZHAO Renda(School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,China;College of Architecture and Civil Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处 《铁道学报》 EI CAS CSCD 北大核心 2018年第7期100-108,共9页 Journal of the China Railway Society
基金 国家自然科学基金(51778531)
关键词 混凝土 徐变 自然环境 相对湿度 温度 预测模型 concrete creep natural environment relative humidity temperature prediction model
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