Hybrid data-driven model for predicting the peak shear strength of rock joints

Statistical and Sensitivity Evaluation of Advanced Computational Models for Predicting Rock Mechanical Properties

The proposed framework provides accurate, robust and interpretable prediction of rock mechanical properties, demonstrating its potential for geotechnical characterization and transportation infrastructure applications.

Applications of Machine Learning Models to Predict the Uniaxial Compressive Strength of Sandstone from Muzaffarabad - A Case Study

Comparative Evaluation of Intelligent Machine Learning Models for Prediction of Uniaxial Compressive Strength of Rocks

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.

Application of data-driven modeling techniques for predicting the shear strength of reinforced concrete beams

Accurate prediction of the shear strength of reinforced concrete (RC) beams remains a challenging problem due to the complex nonlinear interactions among material properties, reinforcement characteristics, and beam geometry. This study presents a data-driven artificial neural network (ANN) framework for predicting th...

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,...

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

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, and results indicate that mineral fibers are more suitable for improving the balanced performance of MS and ITS.