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。

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.