Machine learning models for predicting the compressive strength of agro-waste stabilized bricks for sustainable buildings

Excessive consumption of time and resources are the major challenges of conducting laboratory experiments to determine the mechanical properties of building/construction materials. There is a need to explore various prediction models for mechanical properties of construction materials. This study is aimed at using machine learning models to predict the compressive strength of stabilized bricks for sustainable buildings. Data for the independent variables generated from laboratory experiments were used for the compressive strength prediction. Several machine learning models were explored using Lazy Predict Python library. Extreme Gradient Boosting Regressor with the coefficient of determination (R2) score of 99.45