The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation and aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing waste Polymers into building materials. This study explores the development of eco-friendly bricks incorporating cement, fly ash, M sand, and polypropylene (PP) fibers derived from waste Polymers. The primary innovation lies in leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest and AdaBoost to predict the compressive strength of these Polymer-infused bricks. The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. Machine learning models often function as black boxes, thereby providing limited interpretability; however, our approach addresses this limitation by employing the SHapley Additive exPlanations (SHAP) interpretation method. This enables us to explain the influence of different input variables on the predicted outcomes, thus making the models more transparent and explainable. The performance of each model was evaluated rigorously using various metrics, including Taylor diagrams and accuracy matrices. Among the compared models, the ANN and RF demonstrated superior accuracy which is in close agreement with the experimental results. ANN model achieves R ² values of 0.99674 and 0.99576 in training and testing respectively, whereas RMSE value of 0.0151 (Training) and 0.01915 (Testing). This underscores the reliability of the ANN model in estimating compressive strength. Age, fly ash were found to be the most important variable in predicting the output as determined through SHAP analysis. This study not only highlights the potential of machine learning to enhance the accuracy of predictive models for sustainable construction materials and demonstrates a novel application of SHAP to improve the interpretability of machine learning models in the context of Polymer waste repurposing.

Nowadays, due to the structural advantages gained by combining three different materials' properties, columns made of carbon-fiber reinforced polymer (CFRP)-confined concrete with inner steel tube have received researchers' interest. This article presents the nonlinear finite element analysis and multiple machine learning (ML) model-based study on the behavior of round corner rectangular CFRP-confined concrete short columns reinforced by the inner high-strength elliptical steel tube under the axial load. The reliability of the proposed nonlinear finite element model was verified against the existing experimental investigations. The effects of the parameters such as the concrete grade, thickness of reinforcing steel tube, cross-sectional size of inner steel tube, and thickness of CFRP on the behavior of the columns are comprehended in this study. Furthermore, multiple ML models were proposed to predict the ultimate axial load, ultimate axial strain, and lateral strain of the test specimens. The reliability of the proposed ML models was evaluated by six distinct performance metrics. From the parametric investigation, it was found that concrete with lower compressive strength gained more strength enhancement because of confinement between CFRP and the inner steel tube than high-strength concrete relative to its unconfined compressive strength. The proposed ML models of extreme gradient boosting and random forest provided the best-fit results than the artificial neural network and Gaussian process regression models in predicting the axial load and axial and lateral strains of the columns.