华南理工大学学报(自然科学版)

• 土木建筑工程 • 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

  • 出版日期:2025-07-01 发布日期:2025-07-01

Computational Model of Chloride Diffusion Coefficient of 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

  • Online:2025-07-01 Published:2025-07-01

摘要:

通过对广源大样本RCM试验数据回归分析确定水泥品类因子,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。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,据此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,结果表明广源大样本模型与传统的单源小样本模型相比,可将模型对试验数据的拟合度提升15.7~19.6%;而且水泥品类因子能够合理反映水泥类型和强度的影响,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, 广源大样本

Abstract:

A multi-source large-sample model for chloride diffusion coefficient of concrete was proposed by introducing cement type factor, considering the influence of cement category and strength grade. Firstly, the database with 179 sets of rapid chloride migration (RCM) test data from 70 laboratories was employed for determination of function of chloride diffusion coefficient on water-binder ratio, the type and strength grade of cement through regression analysis. Furthermore, the cement type factor was determined while the computational model was developed for chloride diffusion coefficient of concrete by means of the two-phase regression method. Then, comparison was implemented with different computational models and experimental data, the results show that the multi-source large-sample model has a 15.7~19.6% higher fitting degree to the experimental data than the traditional mono-source small-sample model. Moreover, the cement type factor can reasonably reflect the influence of cement category and strength. By comparing with traditional models and test data, the proposed model is validated with higher predicting accuracy and wider adaptability, reducing the weighted average error by 32.0% and the coefficient of variation by 25.0%.

Key words: concrete; cement type factor, chloride diffusion coefficient, rapid chloride migration, multi-source and large-sample