A machine learning framework that predicts rock cohesion and angle of internal friction from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing is developed.
The depletion of shallow coal resources necessitates the advancement of deep mining operations, where accurate prediction of coal–rock composite mechanical behavior is critical for disaster prevention. This study systematically develops and evaluates a machine learning (ML) framework for predicting key mechanical prope...
The results showed that all models successfully captured the relationship between the input parameters and uniaxial compressive strength, although their predictive capabilities differed considerably, and KStar produced the most accurate predictions.
The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.
Accurate prediction of the mechanical strength of Basalt Fiber Reinforced Concrete (BFRC) is critical for structural design, safety assessment, and the advancement of sustainable infrastructure in civil engineering. Traditional prediction methods often fail to capture the nonlinear relationships between BFRC mix propor...
Three tree-based machine learning models were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset, demonstrating the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datase...
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