Harnessing Nature-Inspired Soft Computing for Reinforced Soil Bearing Capacity Prediction: A Neuro-nomograph Approach for Efficient Design

The use of polyurethane foam-sand mixtures in sandy embankment design- predicting seismic response using FEM, catastrophe theory, B-spline method, and artificial neural networks

“…The main important part of the embankment collapse appeared due to a lack of sufficient tensile strength. To integrate the prediction and classification of the seismic response of the embankment the Multivariate Adaptive Regression Splines (MARS) models, support vectors machines (SVM), Gaussian's process regressions (GPR), and other mathematical modeling could be used to improve the research outcome [ [59] , [60] , [61] ]. As available in the literature, Soft mathematical methods with multilayer frameworks have been applied to solve several problems.…”