An explainable machine learning system for efficient use of waste glasses in durable concrete to maximise carbon credits towards net zero emissions
Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.
| Publié dans: | Waste management (New York, N.Y.). - 1999. - 193(2025) vom: 01. Feb., Seite 539-550 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2025
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| Accès à la collection: | Waste management (New York, N.Y.) |
| Sujets: | Journal Article Carbon credit optimisation Machine learning Sustainable concrete Waste glass concrete Carbon 7440-44-0 |
| Résumé: | Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved. Recycling waste glass (WG) can be time-consuming, costly, and impractical. However, its incorporation into concrete significantly reduces environmental impact and carbon emissions. This paper introduces machine learning (ML) to civil engineering to optimise WG utilisation in concrete, supporting sustainability objectives. By employing a dataset of 471 experimental samples of waste glass concrete (WGC), various ML algorithms are applied, including Gradient Boosting Regressor (GBR), Random Forest (RF), Support Vector Regression (SVR), Adaptive Boosting (AdaBoost), Deep Neural Network (DNN), and k-Nearest Neighbours (kNN), to predict properties containing compressive strength (CS), alkali-silica reaction (ASR), and saved carbon credits (SCC). The proposed models achieve outstanding prediction performance with Coefficient of determination (R2) values of 0.95 for CS, 0.97 for ASR, and 0.99 for SCC using GBR and SVR, demonstrating high prediction accuracy with Root mean square error (RMSE) values of 3.31 MPa for CS, 0.03 % for ASR, and 0.11 for SCC. The SHapley Additive exPlanations (SHAP) analysis is utilised to interpret the model results, ensuring transparency and interpretability of the proposed ML models. The results reveal that the incorporation level of WG is a more significant influencing factor for these properties than the mean size of WG (MSWG) |
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| Description: | Date Completed 29.04.2025 Date Revised 29.04.2025 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1879-2456 |
| DOI: | 10.1016/j.wasman.2024.12.034 |