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

Machine Learning-Driven Prediction and Design Guidance for Asphalt Concrete Using Marshall Stability and Indirect Tensile Strength

This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R2 of 0.85 ± 0.04, whereas TabPFN showed a slight advantage for ITS prediction, achieving an RMSE of 0.23 ± 0.08 MPa and an R2 of 0.91 ± 0.06. Furthermore, Pareto filtering identified nine non-dominated mixtures, and TOPSIS ranking selected the highest-ranked equal-weight compromise mixture, with MS = 15.23 kN and ITS = 3.90 MPa. The results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS, carbon fibers are more favorable for improving MS, and plastic fibers are more effective in improving ITS. Finally, a Streamlit-based graphical user interface was developed to enable real-time prediction and MS–ITS trade-off visualization, providing a reference for preliminary mix design of asphalt concrete.

Application of artificial neural networks for the prediction of traction performance parameters

Machine learning-driven modeling of soil plasticity and strength parameters with interpretability insights

This study proposes advanced stacking ensemble machine learning approaches to predict soil Liquidity Index and Undrained Shear Strength and highlights the robust potential of ensemble modeling and tailored optimization in the field of geotechnical engineering.

Robust Evaluation Framework for Compression Index Prediction Models

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 drilling environments because they primarily rely on traditional models and fail to adequately consider various influencing factors and their nonlinear relationships. To ad-dress these issues, this paper proposes a mechanical penetration rate prediction model based on committee machines. This model effectively captures the variability characteristics of mechanical penetration rates by integrating multiple expert models while employing wavelet filtering methods to denoise the data to enhance data quality. In the application case, this paper collects relevant drilling parameter data based on a vertical well in a specific block. The evaluation of the model shows that it performs excellently in key indicators such as mean square error, coefficient of determination, root mean square error, and mean absolute error, particularly demonstrating a high predictive capability and stability by explaining 97.19% of data variability. The advantage of the constructed model lies in its strong ensemble learning ability, which not only enhances the prediction accuracy of mechanical penetration rates but also helps to deepen the understanding of the dynamic changes in the drilling process, providing effective support for subsequent drilling optimization and resource development.

The Evolution and Future of Machine Learning in Rock Slope Stability: A Systematic Review and Meta-Analysis