Application of an artificial neural network model for predicting unconfined compressive strength of expansive soils

TabPFN-Based Prediction of Concrete Compressive Strength

The use of supplementary cementitious materials such as fly ash can reduce environmental impacts and improve the sustainability of concrete construction. However, the nonlinear interactions among mixture design parameters make accurate prediction of concrete compressive strength challenging. In this study, TabPFN, a pr...

Robust Evaluation Framework for Compression Index Prediction Models

Explainable Levenberg Marquardt trained neural network paradigm for forecasting concrete compressive strength.

In structural engineering, concrete compressive strength (CS) is among its most essential performance characteristics. Although many machine learning models have been developed for predicting this parameter, many suffer from limited transparency. This study developed an accurate and explainable ML model based on the Le...

Reinforcement Loads Prediction of Geosynthetic-Reinforced Soil Structures Using Explainable and Nonexplainable Machine Learning Approaches

This study presents three advanced machine learning models: the evolutionary Gaussian process inference model, the artificial satellite search algorithm–moment balance machine (ASSA-MBM), and the Operation Rain Forest (ORF), which are designed to predict the maximum reinforcement load in geosynthetic-reinforced soi...

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.

Experimental and Machine Learning Approaches for Predicting the Unconfined Compressive Strength of Stabilized Black Cotton Soil