Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19001949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of 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>