Experimental and machine learning investigation for the development of sustainable engineered cementitious composite

Abstract

A machine learning based hybrid modelling and experimental study is presented to advance the design of sustainable engineering cementitious composites (ECC) incorporating recycled constituents. An experimental program was first carried out on ECC mixtures incorporating recycled concrete powder (RCP) as a partial cement replacement and waste tire steel fiber (WTSF) as a reinforcement alternative, as a result indicated a peak compressive strength of 128.5 MPa. After that, a proposed hybrid AI model and benchmark algorithms such as, Artificial Neural Networks, Random Forest, and Gradient Boosting, was trained using 403 experimental data points to predict compressive strengths. The hybrid AI model achieved the highest predictive performance, with coefficient of determination values of 0.96 during training and 0.87 during testing. Overall, the combined experimental and modeling findings demonstrate that the proposed hybrid AI approach can reliably predict compressive performance and accelerate the development of low-carbon cementitious composite.