TL;DR
The proposed framework provides accurate, robust and interpretable prediction of rock mechanical properties, demonstrating its potential for geotechnical characterization and transportation infrastructure applications.
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.
This study investigates an artificial intelligence (AI) based approach for real-time prediction of Young's modulus and UCS using drilling and logging data and reveals that neutron porosity, formation bulk density and Gamma Ray are the three most influential predictors.
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...
This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations r...
Among all ML models, eXtreme gradient boosting demonstrated superior predictive performance, effectively capturing the nonlinear and multivariate interactions governing confinement effectiveness.
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