This paper presents new models to predict chloride penetration into self-compacting concrete (SCC) using the rapid chloride penetration test (RCPT). The research mainly focuses on the effect of supplementary cementitious material (i.e., fly ash and silica fume) and elevated temperature curing of SCC on results of the RCPT. Models are developed to predict the value of RCPT using two statistical algorithms, namely Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR). Both models incorporate the combined effect of fly ash, silica fume and elevated temperature curing on the RCPT, and a comparative study between the models is also discussed. The analysis confirms that both MARS and MPMR are promising models for the prediction of RCPT results.

本文介绍了使用快速氯离子渗透测试（RCPT）预测氯离子渗透到自密实混凝土（SCC）中的新模型。研究主要集中在补充胶凝材料（如粉煤灰和硅粉）和高温下SCC对RCPT结果的影响。使用两种统计算法开发模型来预测RCPT的值，即多元自适应回归样条（MARS）和最小极大概率机器回归（MPMR）。两种模型都结合了粉煤灰，硅粉和高温固化对RCPT的综合作用，并且还讨论了模型之间的比较研究。分析证实，MARS和MPMR都是预测RCPT结果的有前途的模型。