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Open access Aug 2026

Geotechnical Evaluation of Gradient-Based Neural Networks for Factor of Safety Prediction in Homogeneous Soil Slopes Under Hydraulic Variability

Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi...

Shaza Soleiman, M. Rahhal · 0 citations
Open access Jul 2026

Predictive Modelling and Optimization of Slope Stability Using Numerical Simulations and Machine Learning Techniques

The current study combines numerical modelling and machine learning to identify the stability of applications in nail reinforced slope study. PLAXIS LE was used to develop different slopes having different soil properties including various values for cohesion (5, 10, 15 kPa), angle of internal friction (20°, 25°, 30°),...

Radha Tomar, Smita Tung · 0 citations
Open access Aug 2026

Optimizing Machine Learning Models for Predicting Rock Cohesion and Angle of Internal Friction: A Comparative Study of Lithological Analysis, Robustness Assessment, and SHAP Explanations

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.

Jiangquan Xie, Xue-Bin Xie · 0 citations
Conference Open access Aug 2026

Data-Driven Mechanical ROP Prediction: Construction and Validation of Committee Machine Model

With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drillin...

Tao Cai, Huai-Yan Qi, Xue-Wu Yang et al. · 0 citations
Jul 2026

An Insight Into Rock Brittleness Prediction Based on Stacking Integrated Algorithm

The significance of rock brittleness is well‐recognized in the fields of geotechnical engineering and energy exploration. To enhance the predictive precision of rock brittleness, this paper proposes a Stacking integrated algorithm. This algorithm synergistically combines various meta models and foundational models,...

Jing Jia, Diquan Li, Ziyi Zhang et al. · 0 citations

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