Corrosion initiation time of embedded steel is an important service life parameter, which depends on concrete material make-up, exposure environment, and duration of exposure. Early and accurate determination of corrosion initiation time will aid in designing durable reinforced concrete, saves cost and time. This study leveraged on the power of ensemble machine learning by combining the performances of different models in estimating the corrosion initiation time of steel embedded in self compacted concrete using corrosion potential measurement. The concrete specimens were prepared with limestone powder as supplementary addition to Portland cement and was exposed to 5% sodium chloride in accordance with the requirements of ASTM C876 – 15 for 8 months. During the exposure, corrosion potential of the embedded steel was measured, and the recorded datasets were used in training five different machine learning models. With cement, limestone powder, coarse aggregate, fine aggregate, water and exposure period.as input variables, five different models were developed to estimate the corrosion initiation time (determined from the corrosion potential measurements) of the embedded steel. With respect to model predictive performance, the acquired results demonstrated that the random forest (RF) ensemble model amongst other trained models performed best with 85/15 dataset percentage split for the training and testing. RF ensemble performed best with CC and RMSE of 99.01% and 18.2747 mV for training, and 98.67% and 25.0298 mV for testing respectively. Hence, due to its superior and robust performance, this study proposes RF ensemble model in the estimation of corrosion initiation time of embedded steel in reinforced limestone-cement blend concrete.

嵌入式钢的腐蚀起始时间是重要的使用寿命参数，它取决于混凝土材料的组成，暴露环境和暴露持续时间。尽早而准确地确定腐蚀起始时间将有助于设计耐用的钢筋混凝土，从而节省成本和时间。这项研究通过结合不同模型的性能来利用整体机器学习的能力，从而利用腐蚀电位测量来估算埋入自密实混凝土中的钢的腐蚀起始时间。混凝土样品是用石灰石粉作为波特兰水泥的补充剂制备的，并按照ASTM C876 – 15的要求暴露于5％氯化钠中8个月。在暴露期间，测量了嵌入钢的腐蚀电位，记录的数据集用于训练五种不同的机器学习模型。以水泥，石灰石粉，粗骨料，细骨料，水和暴露时间为输入变量，开发了五个不同模型来估计埋藏钢的腐蚀起始时间（由腐蚀电位测量确定）。关于模型的预测性能，获得的结果表明，在其他训练有素的模型中，随机森林（RF）集成模型在训练和测试中以85/15的数据集百分比分割表现最佳。RF集成表现最佳，CC和RMSE在训练中分别为99.01％和18.2747 mV，在测试中分别为98.67％和25.0298 mV。因此，由于其卓越而强大的性能，