Implementing The Multi Expression Programming, Nonlinear Regression, Artificial Neural Network, and M5P-Tree Models to Predict The Long-Term of Compressive Strength of Cement- Mortar Modified With Fly Ash

Abstract In the recent decade, supplementary cementing ingredients have become an essential part of various strength ranges of concrete and cement-mortar mix design. Examples are natural materials, by-products, industrial wastes, and materials that require less energy and time to generate. Fly ash is one of the most widely utilized additional cementing ingredients. Fly ash is a by-product substance produced by coal combustion. It's being used in cement mortar and concrete as a pozzolanic substance. It has demonstrated significant influence in improving liquid and solid properties of cement mortar, such as compressive strength. Multi Expression Programming (MEP) is employed in this study to estimate the compressive strength (CS) of cement mortar modified with fly ash. The outcomes of this model were compared and evaluated with several other models such as the Nonlinear Regression model (NLR), Artificial Neural Network (ANN), and M5P-tree models that have been used in the construction fields. The input parameters included water/cement ratio (w/c), curing time (t days), and fly ash content (FA %), while the target property was compressive strength up to 360 days of curing. Four hundred fifty (450) data are collected from previous literature on modifying cement mortar with fly ash for that purpose. The water/cement ratio ranged from 0.24 to 1.2, and the fly ash was used to replace cement up to 55% (%wt. of dry cement). Based on the Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Scatter Index (SI), Objective (OBJ), Mean Absolute Error (MAE), t-test value, the uncertainty of 95%, Performance Index (ρ), and boxplot for actual and predicted compressive strength. The MEP model performed better than other developed models according to evaluation tools. The compressive strength was also correlated with flexural and splitting tensile strengths using different nonlinear models.