Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

In order to reduce the adverse effects of concrete on environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically environmentally sustainable alternative portland cement. This is accomplished through utilization alumina-silicate waste materials as a cementitious binder. These synthesized by activating minerals with alkali. paper employs three-step machine learning (ML) approach in estimate compressive strength geopolymer concrete. The ML methods include CatBoost regressors, extra trees gradient boosting regressors. addition 84 experiments literature, 63 were constructed tested. Using Python language programming, models built from 147 samples four variables. Three these combined using blending technique. Model performance was evaluated several metric indices. Both individual hybrid predict high accuracy. However, model claimed able improve prediction accuracy 13%.