标准碳化环境下基于材料参数的混凝土碳化深度多因素计算模型
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  • 英文篇名:Multi-factor Computation Model of Concrete Carbonation Depth Based on Material Parameters in Standard Carbonization Environment
  • 作者:陈正 ; 胡以婵 ; 赵宇飞 ; 余波
  • 英文作者:CHEN Zheng;HU Yi-chan;ZHAO Yu-fei;YU Bo;Guangxi Key Laboratory of Disaster Prevention and Structural Safety,Key Laboratory of Engineering Disaster Prevention and Structural Safety of China Ministry of Education,School of Civil Engineering and Architecture,Guangxi University;Guangxi Road and Brioge Engineering Group Co.,Ltd;Houqiao Subdistrict Office of Xishan People's Government;
  • 关键词:混凝土 ; 耐久性 ; 碳化深度 ; 多因素计算模型
  • 英文关键词:concrete;;durability;;carbonation depth;;multi-factor computation model
  • 中文刊名:GSYT
  • 英文刊名:Bulletin of the Chinese Ceramic Society
  • 机构:广西大学土木建筑工程学院工程防灾与结构安全教育部重点实验室广西防灾减灾与工程安全重点实验室;广西路桥工程集团有限公司;无锡市锡山区人民政府厚桥街道办事处;
  • 出版日期:2019-06-15
  • 出版单位:硅酸盐通报
  • 年:2019
  • 期:v.38;No.273
  • 基金:国家自然科学基金(51468004)
  • 语种:中文;
  • 页:GSYT201906007
  • 页数:8
  • CN:06
  • ISSN:11-5440/TQ
  • 分类号:42-48+54
摘要
为了准确评估混凝土的抗碳化性能和耐久性,根据混凝土碳化深度分析的实用预测模型获得了7822组标准碳化环境条件(温度为(20±2)℃,相对湿度为(70±5)%,CO_2浓度为(20±3)%)下普通混凝土、单掺粉煤灰混凝土、单掺矿渣混凝土以及复掺粉煤灰和矿渣混凝土的碳化深度数据,定量分析了水胶比(或水灰比)、粉煤灰掺量和矿渣掺量等对混凝土碳化深度的影响规律,进而采用基于最小二乘法的非线性回归分析,基于材料参数建立了标准碳化环境下混凝土碳化深度多因素计算模型,并通过文献搜集的试验数据验证了该方法提出的模型的合理性和适用性。
        In order to accurately evaluate the carbonation resistance and durability of concrete,7822 sets of date of carbonation depth for ordinary concrete,fly ash concrete,slag concrete,fly ash and slag concrete in the standard carbonization environment( temperature is of( 20 ± 2) ℃,relative humidity is of( 70 ± 5) %,CO_2 concentration is of( 20 ± 3) %) was generated according to the practical carbonation prediction model. Then the influences of water-cement ratio( or water-binder ratio) and mineral admixtures on carbonation depth of concrete were analyzed. Meanwhile,a multi-factor computation model for concrete carbonation depth in terms of material parameters under the standard carbonization environment was developed by means of the nonlinear regression technique based on the least square method. Finally,the accuracy and applicability of the proposed model was validated by comparing with experimental data.
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