ArticleScientific reports2025
Ensemble machine learning models for predicting strength of concrete with foundry sand and coal bottom ash as fine aggregate replacements.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimizing the mechanical performance of sustainable industrial waste modified concrete using supervised machine learning modeling and feature importance analysis.Scientific reports · 2026Article
- A machine learning framework for structural and predictive analysis of intelligent data networks.Scientific reports · 2026Article
- Compressive strength of mortar incorporating recycled nonmetallic printed circuit board waste: experimental and interpretable machine learning analysis.Scientific reports · 2026Article
- Hybrid machine learning approach for predicting compressive strength of sustainable concrete incorporating palm oil fuel ash.Scientific reports · 2026Article
- A hybrid flower pollination algorithm-deep learning approach for strength prediction of sustainable recycled fine aggregate concrete with GUI-based implementation.Scientific reports · 2026Article
- Integration of machine learning and microstructural characterization for strength forecasting with silica fume and M-sand for sustainable concrete.Scientific reports · 2026Article
- Predicting concrete compressive strength using optimized deep learning and large language models.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Natural sand scarcity and environmental concerns have encouraged the adoption of industrial waste materials in sustainable concrete. Foundry sand (FS) and coal bottom ash (CBA), both industrial waste materials, offer potential as partial replacements for natural sand. However, predicting the compressive strength (CS) of such mixes is complex due to nonlinear interactions among their components. This study proposes a novel, machine learning-based framework for predicting the CS of concrete incorporating FS and CBA. A dataset of 172 mix designs was compiled from published literature. Nine machine learning models were evaluated, including traditional regressors and ensemble methods. The Extreme Gradient Boosting (XGBoost) model achieved the highest accuracy, with R
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Registered trials
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