华南理工大学学报(自然科学版) ›› 2025, Vol. 53 ›› Issue (12): 153-160.doi: 10.12141/j.issn.1000-565X.240573

• 材料科学与技术 • 上一篇    下一篇

考虑水泥品类影响的混凝土氯离子扩散系数模型

朱恩, 杨绿峰   

  1. 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 南宁 530004
  • 收稿日期:2024-12-06 出版日期:2025-12-25 发布日期:2025-07-01
  • 通信作者: 杨绿峰(1966—),男,教授,博士生导师,主要从事混凝土结构耐久性与结构承载力及其优化设计研究。 E-mail:lfyang@gxu.edu.cn
  • 作者简介:朱恩(1990—),男,博士生,主要从事混凝土耐久性设计研究。E-mail: zhuenlab@163.com
  • 基金资助:
    广西科技重大专项(桂科AA23023018);广西重点研发计划项目(桂科AB23026026)

Computational Model for Chloride Diffusion Coefficient in Concrete Considering the Influence of Cement Type and Strength

ZHU En, YANG Lufeng   

  1. School of Civil Engineering and Architecture/Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education,Guangxi University,Nanning 530004,Guangxi,China
  • Received:2024-12-06 Online:2025-12-25 Published:2025-07-01
  • Contact: 杨绿峰(1966—),男,教授,博士生导师,主要从事混凝土结构耐久性与结构承载力及其优化设计研究。 E-mail:lfyang@gxu.edu.cn
  • About author:朱恩(1990—),男,博士生,主要从事混凝土耐久性设计研究。E-mail: zhuenlab@163.com
  • Supported by:
    the Guangxi Major Science and Technology Project(AA23023018);the Guangxi Key Research and Development Project(AB23026026)

摘要:

通过对广源大样本RCM(氯离子快速迁移)试验数据的回归分析确定水泥品类因子,并据此建立考虑水泥类型和强度等级影响的混凝土氯离子扩散系数计算模型,以此克服传统计算模型难以通过不同试验室交叉检验的缺陷。首先,基于来源于70个不同试验室的179组混凝土RCM试验数据形成的氯离子扩散系数的广源大样本试验数据库,通过回归分析确定水胶比、水泥类型及强度等级对氯离子扩散系数的影响规律;然后,利用广源大样本试验数据和两相回归法,在混凝土氯离子扩散系数计算模型中引入水泥品类因子,并确定其取值,从而建立考虑水泥类型和强度等级影响的氯离子扩散系数模型;最后,与传统模型开展对比分析,并利用非建模试验数据进行验证。结果表明:与传统的单源小样本模型相比,广源大样本模型可将模型对试验数据的拟合度提升19.6%;水泥品类因子能够合理反映水泥类型和强度等级的影响,据此建立的扩散系数模型与传统模型相比,可进一步将模型加权平均误差和变异系数分别降低32.0%和25.0%,从而大幅提升了模型的预测精度和适应性。

关键词: 混凝土, 水泥品类因子, 氯离子扩散系数, 氯离子快速迁移(RCM), 广源大样本

Abstract:

To address the limitations of traditional models in cross-laboratory validation, this study proposes a chloride diffusion coefficient model for concrete that incorporates a cement type factor, accounting for the influence of cement type and strength grade. The model is developed through regression analysis of multi-source large-sample Rapid Chloride Migration (RCM) test data. Firstly, a comprehensive database of 179 RCM test datasets from 70 laboratories was established to analyze the effects of water-binder ratio, cement type, and strength grade on the chloride diffusion coefficient via regression. Furthermore, the cement type factor was introduced into the computational model using a two-phase regression method, and its value was determined based on the multi-source large-sample data. Finally, comparative analyses with traditional models and validation using independent test data were conducted. The results show that the proposed multi-source large-sample model improves the fitting accuracy to experimental data by 19.6% compared to conventional mono-source small-sample models. The cement type factor effectively captures the combined influence of cement type and strength, reducing the weighted average error and coefficient of variation by 32.0% and 25.0%, respectively, thereby significantly enhancing the model’s predictive precision and adaptability.

Key words: concrete, cement type factor, chloride diffusion coefficient, rapid chloride migration(RCM), multi-source large-sample

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