Predicting the Effects of Nano Additives and Elevated Temperatures on Concrete Compressive Strength Utilizing Machine Learning

In this study, the synergistic effects of a combination of nano additives (nano-clay (NC) and nano-silica (NS)) on the compressive strength (CS) of concrete exposed to temperatures ranging between 25 °C and 800 °C were modeled with two machine learning (ML) techniques: extreme gradient boosting (XGB) and random forest (RF) algorithms. A dataset comprising 169 compressive strength results (using four input parameters: NC dose, NS dose, temperature, and duration) was utilized for the raw data for the prediction models. The results indicated the superior performance of the XGB model in terms of the high accuracy attained in the prediction and the few errors present. Furthermore, SHAP analysis demonstrated that temperature has the highest negative impact on the prediction of the CS of nano-modified concrete. The individual conditional expectation (ICE) with partial dependence plots (PDPs) demonstrated that the optimum doses of NS and NC, leading to maximum compressive strength, were (2~3%) and (5~6%) by weight of cement. The developed models can be used as tools for optimizing mix designs to enhance fire resistance, thereby contributing to more durable and sustainable concrete construction and reducing the need for costly experimental trials.