Prediction on mechanical properties of engineered cementitious composites: An experimental and machine learning approach

Abstract The adoption of engineered cementitious composites (ECC) has witnessed a notable surge in recent years, primarily because of their remarkable strain‐hardening behavior and other hardened properties. On the other hand, machine learning (ML) approaches have been widely employed to predict various properties in engineering applications by incorporating an ‘ n ’ number of inputs and target data. An ML aids in understanding the selection, properties, and blending ability of materials, thereby reducing the cost and duration of research. In this study, an ML technique based on an artificial neural network (ANN) is utilized to predict the mechanical properties of ECC with different binders. For this purpose, several parameters are collected from the present investigation and various literature sources, and the collected data are trained using three algorithms, namely, scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg–Marquardt (LM). The correlations and variations between the experimental and predicted outputs are analyzed. In addition, a comparison between the experimental results obtained by each investigator and the corresponding outputs predicted by the individual algorithms is highlighted. The LM algorithm achieved a mean regression value of 0.910 for the prediction of compressive strength, whereas the BR showed values of 0.908 and 0.852 for predicting the direct tensile and flexural properties of ECC, respectively. Furthermore, considering the standard benchmark, the proposed model exhibited a high correlation with the coefficient of determination ( R 2 ).