Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network

Abstract

The California bearing ratio (CBR) is an important parameter in defining the bearing capacity of various soil structures, such as earth dams, road fillings and airport pavements. However, determination of the CBR value of compacted soils from tests takes a relatively long time and leads to a demanding experimental working program in the laboratory. This study is aimed to predict the CBR value of compacted soils by using the group method of data handling (GMDH) model with a type of artificial neural networks (ANN). The results were also compared with multiple linear regression (MLR) analysis and different ANN models. The selected variables for the developed models are gravel content (GC), sand content (SC) fine content (FC), liquid limit (LL), plasticity index (PI), optimum moisture content (OMC) and maximum dry density (MDD) of compacted soils. Many trials were carried out with different numbers of layers and different numbers of neurons in the hidden layer in GMDH model and with different training algorithms in ANN models. The results indicate that the GMDH model has better success in the estimation of the CBR value compared to both the MLR and the different types of ANN models.