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

Abstract

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% and root mean square error of 0.06 outperformed all the tested models. SHAP (SHapley Additive exPlanations) analysis was used to show the distribution of the impacts of the features on the predicted compressive strength. The SHAP analysis results showed that higher percentages of the stabilizers (with reference to 0%, 2%, 4%, and 6% of stabilizers) have a positive impact on the prediction of compressive strength of stabilized bricks. In addition, higher water content, lower lateritic soil contents, lower cube sizes and curing at higher values of temperature favoured the prediction of compressive strength of stabilized bricks. The proposed model can be adapted by civil engineers and researchers for the prediction of the compressive strength of agro-waste stabilized construction materials. The implication of this study will save the time and resources required by the construction professionals to obtain the compressive strength agro-waste stabilized bricks during the production of bricks for sustainable construction applications.