Comparative Evaluation of Feed-Forward Neural Networks for Predicting Uniaxial Compressive Strength of Seybaplaya Carbonate Rock Cores

Accurate estimation of the uniaxial compressive strength (UCS) of carbonate rocks underpins safe design and stability assessment in karst-influenced geotechnical projects. This work presents a comprehensive evaluation of four feed-forward artificial neural network (ANN) architectures—radial basis function (RBF), Bayesian regularized (BR), scaled conjugate gradient (SCG), and Levenberg–Marquardt (LM)—to predict UCS from three readily measured variables: water content, interconnected porosity, and real density. Fifty core specimens from the Seybaplaya quarry in Campeche, Mexico, were split into training and testing subsets under uniform preprocessing. Each model’s predictive performance was assessed over 30 independent runs using mean absolute error, root mean squared error, and coefficient of determination, with statistical differences tested via nonparametric hypothesis testing. The RBF network achieved the highest median R2 and significantly outperformed the other variants, while the BR model demonstrated robust generalization. SCG and LM converged faster and efficiently but with slightly lower accuracy. Sensitivity analysis identified interconnected porosity as the primary predictor of UCS. These results establish RBF-based ANNs with appropriate regularization and feature importance assessment as a novel, practical, and reliable framework for UCS prediction in heterogeneous carbonate formations.