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Open access
Jul 2026
Prediction of Backfill Slurry Shrinkage Rate and Optimization of Roof-Contact Backfilling Based on an Optimized Neural Network Approach
In backfill mining of metal mines, the sedimentation and shrinkage of backfill slurry critically affect roof-contact quality and stope stability. This study investigates these characteristics for unsorted tailings backfill slurry from the Daye Iron Mine through laboratory experiments and develops a prediction model for...
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· Applied Sciences · 1 citation
Open access
Aug 2026
Explainable Random Forest Framework for Predicting Compressive Strength of Sustainable Concrete Incorporating Industrial Waste Materials
Compressive strength is the single most important design parameter governing the safety, serviceability, and economy of concrete structures, yet its determination through standard 7-, 14-, or 28-day destructive cylinder/cube testing is slow, costly, and unable to assess concrete already cast in place. This study develo...
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Review
Nov 2026
Precision Assessment of Data-Driven Supervised Machine-Learning Models for Predicting Compressive Strength of Sustainable Waste Foundry Sand Concrete
The rapid rate of urbanization and industrialization has driven the excessive use of natural resources like river sand and gravel, raising significant sustainability concerns. Waste foundry sand (WFS), a discarded by-product of ferrous and nonferrous metal casting industries, offers a promising substitute for natur...
M. H. R. Sobuz, Md. Kawsarul Islam Kabbo, A. Alzlfawi et al.
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Open access
Aug 2026
Bayesian-optimised machine learning for predicting aggressive environment resistance and service life of recycled aggregate geopolymer concrete
Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recyc...
P. Singh, Puja Rajhans
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Preprint
Aug 2026
Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
An interpretable machine-learning framework for predicting the splitting strength of asphalt concrete and supporting data-driven mixture design and a GUI platform integrating prediction and SHAP-based explanation was developed to improve the accessibility and practical applicability of the proposed framework.
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Open access
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Performance Prediction and Ratio Design of Coal-Based Solid Waste Cemented Filling Materials Based on Ensemble Learning
Coal-based solid wastes, including coal gangue and fly ash, can be extensively utilised in cemented backfill materials. However, the slump, bleeding rate, and mechanical strength of these materials depend nonlinearly on the mixture composition, particle size, solids concentration, and curing conditions, complicating th...
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