Waste glass (WG) can be used as fine aggregate and powder in concrete mixtures, preventing pollution induced by this non-biodegradable material. The properties of WG-included concrete should be examined before its practical use. Compressive strength (CS) is one of the most crucial characteristics of concrete, and the measurement of which needs time-consuming and expensive experiments. The use of machine learning (ML) methods for modeling the CS of concrete can help achieve more reliable and precise models. In this study, a comprehensive database of WG-included concrete was collected from the literature. Next, four ML methods, including support vector regression (SVR), least-square support vector regression (LSSVR), adaptive neuro-fuzzy inference system (ANFIS), and multilayer perceptron neural network (MLP) were served in the CS modeling. A recently proposed metaheuristic method, called marine predators algorithm (MPA), was proposed to optimize the control parameters of the ML models to guarantee generalized accuracy. Results indicate that the hybrid LSSVR-MPA model outperforms the other developed ML models comparing the error metrics with an RMSE = 2.447 MPa and R2 = 0.983. The sensitivity analysis reveals that replacing the cement with WG powder decreases the CS, whereas serving the WG as the replacement for natural fine aggregate improves the CS.
- compressive strength
- machine learning
- metaheuristic optimization algorithm
- waste glass
- waste materials