Computing Models to Predict the Compressive Strength of Engineered Cementitious Composites (ECC) at Various Mix Proportions

Abstract

Concrete has relatively high compressive strength (resists breaking when squeezed) but significantly lower tensile strength (vulnerable to breaking when pulled apart). The compressive strength is typically controlled by the ratio of water-to-cement when forming the concrete, and tensile strength is increased by additives, typically steel, to create reinforced concrete. In other words, we can say concrete is made up of sand (which is a fine aggregate), ballast (which is a coarse aggregate), cement (which can be referred to as a binder), and water (which is an additive). Highly ductile material engineered cementitious composites (ECC) were developed to address these issues by spreading short polymer fibers randomly throughout a cement-based matrix. It has a high tensile strain capacity of more than 3%, hundreds of times more than conventional concrete. On the other hand, among the other examined qualities, compressive strength (CS) is a critical property. Consequently, developing reliable models to predict an ECC’s compressive strength is crucial for cost, time, and energy savings. It also includes instructions for planning construction projects and calculating the optimal time to remove the formwork. The artificial neural network (ANN), nonlinear model (NLR), linear relationship model (LR), multi-logistic model (MLR), and M5P-tree model were all proposed as alternative models to estimate the CS of ECC mixtures created by fly ash in this research (M5P). To create the models, a large amount of data were gathered and evaluated, totaling roughly 205 mixes. Various mixture proportions, fiber length, diameter, and curing durations were explored as input variables. To test the effectiveness of the suggested models, several statistical evaluations, including determination coefficient (R2), Mean Absolute Error (MAE), Scatter Index (SI), Root Mean Squared Error (RMSE), and Objective (OBJ) value, were utilized. Based on the statistical evaluations, the ANN model performed better in forecasting the CS of ECC mixes incorporating fly ash than other models. This model’s RMSE, MAE, OBJ, and R2 values were 4.55 MPa, 3.46 MPa, 4.39 MPa, and 0.98, respectively. A large database presented in this investigation can be used as the bench mark for future mixture proportions of the ECC. Moreover, the sensitivity analysis showed the contribution of each mixture ingredient on the CS of ECC.