This study investigates the applicability of two artificial intelligence (AI) techniques, namely,
the support vector regression (SVR) and artificial neural network (ANN), in prediction of the …

Selecting the appropriate training technique is a significant step in utilizing intelligent
approaches. It becomes even more important when it comes to critical problems like …

This paper presents the application of artificial neural networks (ANN) and multivariable
regression analysis (MRA) to predict the bearing capacity and the settlement of multi-edge …

In this study an Artificial Neural Network (ANN) model was used to predict the Unconfined
Compressive Strength (UCS) of Kaolin clay mixed with pond ash, rice husk ash and cement …

The mechanical properties of soil and rock in general have significant non-linearity, this is
due to the very complex composition of soil and rock [1]. Therefore, conventional methods in …

The current study uses machine learning techniques such as Random Forest Regression
(RFR), Artificial Neural Networks (ANN), Support Vector Machines Ploy kernel (SVMP) …

The paper presents the prediction of the ultimate bearing capacity of the strip footing resting
on layered soil (dense sand overlying loose sand) using random forest regression (RFR). In …

Generally, expansive soils undergoes significant volumetric deformation, which causes
structural damages to existing infrastructures. Damages due to expansive activities are …

Urbanization and associated infrastructure development keep developing along the globe in
today's reality. In this case, the utility of every single weak land (soft soils) in the city is …

Prediction of the free swell index of the expansive soil using artificial neural network has
been presented in this paper. Input parameters for the artificial neural network model were …