This paper investigates the potential of a Gaussian process (GP) regression approach to
predict the load-bearing capacity of piles. Support vector machines (SVM) and empirical …

This note investigates the potential of support vector machines based regression approach
to model the static pile capacity from dynamic stress-wave data. A data set of 105 …

Thin-walled spread foundations are used in coastal projects where the soil strength is
relatively low. Developing a predictive model of bearing capacity for this kind of foundation is …

This paper presents the application of the Artificial Neural Network (ANN) for prediction of
axial capacity of a driven pile by adopting data collected from several projects in Indonesia …

Determining footing design parameters is crucial in designing buildings and other
geotechnical structures. Bearing capacity and settlement of foundations are two important …

M5 model tree, random forest regression (RF) and neural network (NN) based modelling
approaches were used to predict oblique load carrying capacity of batter pile groups using …

Geotechnical engineers frequently rely on semi-empirical methods like Décourt-Quaresma
and Meyehof's to estimate the bearing capacity of piles. This paper proposes alternatives to …

This paper report the results of generalized regression neural network (GRNN) based
modelling approach to predict the load-bearing capacity of piles. Three empirical relations …

A few decades, many methods have been developed to predict and evaluate the bearing
capacity of driven piles. The problem of the predicting and assessing the bearing capacity of …

The study presents advanced hybrid artificial neural network (ANN) models to enhance the
analysis of slope stability and the prediction of the factor of safety (FOS). Conventional …