Gorde:
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| Formatua: | Recurso digital |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Zenodo
2026
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| Gaiak: | |
| Sarrera elektronikoa: | https://doi.org/10.5281/zenodo.19001949 |
| Etiketak: |
Etiketa erantsi
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| _version_ | 1866902125142867968 |
|---|---|
| author | Shambhavi Sinha |
| author_facet | Shambhavi Sinha |
| contents | <p><span>This study harnesses machine learning to innovate marble waste recycling, delivering a novel, data-driven solution for sustainable construction and industrial applications. Utilising a dataset of 20,000 records, the research pinpointed particle size as a pivotal factor, with finer particles (<10 µm) ideal for Calcium Carbonate production and larger particles (>50 µm) suited for Aggregates. Exploratory data analysis, conducted with precision, revealed significant particle size variation across waste types (ANOVA: F=36.26, p=2.34e-23), guiding meticulous feature engineering, including particle size binning and interaction terms. Three classification models, Random Forest, XGBoost, and Logistic Regression, were rigorously developed, with SMOTE addressing class imbalance. Post-SMOTE, Random Forest achieved a macro-averaged F1-score of 0.52, markedly improving minority class predictions (Calcium Carbonate: 0.49; Other: 0.30), though overall accuracy (0.57) reflects trade-offs in majority class performance. Feature importance and SHAP analyses, clearly presented, underscored Waste Type’s dominance (r=0.683) and particle size’s critical role. </span></p> <p><span lang="EN-US"> </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19001949 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing Marble Waste Recycling Through Machine Learning: The Role of Particle Size Variation and Class Imbalance Mitigation Shambhavi Sinha waste recycling, sustainable construction, ANOVA, Random Forest, XGBoost, and Logistic Regression <p><span>This study harnesses machine learning to innovate marble waste recycling, delivering a novel, data-driven solution for sustainable construction and industrial applications. Utilising a dataset of 20,000 records, the research pinpointed particle size as a pivotal factor, with finer particles (<10 µm) ideal for Calcium Carbonate production and larger particles (>50 µm) suited for Aggregates. Exploratory data analysis, conducted with precision, revealed significant particle size variation across waste types (ANOVA: F=36.26, p=2.34e-23), guiding meticulous feature engineering, including particle size binning and interaction terms. Three classification models, Random Forest, XGBoost, and Logistic Regression, were rigorously developed, with SMOTE addressing class imbalance. Post-SMOTE, Random Forest achieved a macro-averaged F1-score of 0.52, markedly improving minority class predictions (Calcium Carbonate: 0.49; Other: 0.30), though overall accuracy (0.57) reflects trade-offs in majority class performance. Feature importance and SHAP analyses, clearly presented, underscored Waste Type’s dominance (r=0.683) and particle size’s critical role. </span></p> <p><span lang="EN-US"> </span></p> |
| title | Enhancing Marble Waste Recycling Through Machine Learning: The Role of Particle Size Variation and Class Imbalance Mitigation |
| topic | waste recycling, sustainable construction, ANOVA, Random Forest, XGBoost, and Logistic Regression |
| url | https://doi.org/10.5281/zenodo.19001949 |