An interpretable XGBoost-SHAP machine learning model for reliable prediction of mechanical properties in waste foundry sand-based eco-friendly concrete
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Abstract
Construction and development projects worldwide heavily rely on concrete as their primary building material, making it an essential component of global infrastructure and growth. The various key ingredients that compose concrete contribute differently to its overall environmental footprint. Moreover, rapid urban and industrial growth has strained ecological systems and depleted resources, necessitating environmentally friendly substitutes for traditional concrete ingredients. Waste foundry sand has emerged as a potential replacement for natural sand in concrete mixtures, offering a sustainable option. Since evaluating waste foundry sand effects on concrete through laboratory methods is resource-intensive,…
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66
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- FWCI
- 37.60
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- 100%
- References
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Authors
7Topics & keywords
Topics
Keywords
- Foundry
- Machine building
- Environmentally friendly
- Sand casting
- Machine learning
- Computer science
- Environmental science
- Process engineering
UN Sustainable Development Goals
- Responsible consumption and production
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