The efficiency of hybrid intelligent models to evaluate the effect of the size of sand and clay metakaolin content on various compressive strength ranges of cement mortar

This research aims to create a reliable model for estimating the compressive strength of cement mortar amended with metakaolin (MK) ingredients and predicting the impact of MK and the maximum diameter of the fine material (MDA) on the compressive strength of the mortar. This study collected 230 datasets from the research with various percentages and curing times. The water-to-binder ratio (w/b) ranges between 0.36 and 0.6 (by the weight of dry cement), sand to-binder ratio of 2–3, the metakaolin content of 0–30%, and the curing time is up to 90 days. Artificial neural network (ANN) models, nonlinear regression (NLR), multi-expression programming (MEP), and multivariate regression spline (MARS) models are the models. The coefficient of determination (R²), root-mean-squared error (RMSE), mean absolute error (MAE), scatter index (SI), standard deviation, and correlation coefficient between measured and predicted compressive strength are some of the evaluation tools that are used to measure the performance of the suggested models. The results show that the MARS model performs better with a high R² and low RMSE and MAE than MEP, NLR, and ANN models. Based on the dispersion index, the MARS, MEP, and ANN, they have made good predictions about the compressive strength. According to the parametric analysis of MK and MDA, the ANN model effectively anticipated the impact of the previously mentioned model inputs and the ideal MK concentration for enhancing both long- and short-term compressive strength.