No abstract

Strain‐hardening cement‐based composites or engineered cementitious composites (ECC) is concrete produced using randomly distributed short polymer fibers. It is very ductile compared to conventional concrete. Compressive strength (CS) is a critical property used as a quality control tool to evaluate the strength of concrete implemented in the structural provisions and mix designs. Accordingly, to save cost and time for testing, it is essential to provide a predictive model to forecast the CS of the concrete mixtures using machine learning and modeling techniques. In this study, different modeling tools are used to propose analytical models to predict the CS of ECC mixtures, such as linear regression (LR), multi‐expression programming (MEP), artificial neural network (ANN), and Gaussian process regression (GPR). A total of 210 data were collected from the literature and used to train and test the developed model. The fly ash‐to‐cement ratio ranged from 0 to 5.6, water to binder ratio from 0.19 to 0.56, different superplasticizer and fiber content, and curing times of 1 to 180 days. Based on the evaluation of the developed models, the ANN model is superior to other developed models with a high coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and scatter index (SI). The sensitivity analysis of the input parameters' effect on the CS prediction indicates that the curing time and the fly ash‐to‐cement ratio are essential in forecasting ECC's CS.