Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability DOI Creative Commons
S. Sathvik, Rakesh Kumar,

Archudha Arjunasamy

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

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

Language: Английский

Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability DOI Creative Commons
S. Sathvik, Rakesh Kumar,

Archudha Arjunasamy

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

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

Language: Английский

Citations

1