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Öğe A novel ensemble model based on GMDH-type neural network for the prediction of CPT-based soil liquefaction(Springer, 2019) Kurnaz, T. Fikret; Kaya, YilmazThis study presents a novel ensemble group method of data handling (EGMDH) model based on classification for the prediction of liquefaction potential of soils. Liquefaction is one of the most complex problems in geotechnical earthquake engineering. The database used in this study consists of 212 CPT-based field records from eight major earthquakes. The input parameters are selected as cone tip resistance, total and effective stress, penetration depth, max peak horizontal acceleration and earthquake magnitude for the prediction models. The proposed EGMDH model results were also compared to the other classifier models, particularly the results of the group method of data handling (GMDH) model. The results of this study indicated that the proposed EGMDH model has achieved more successful results on the prediction of the liquefaction potential of soils compared to the other classifier models by improving the prediction performance of the GMDH model.Öğe Prediction of the California bearing ratio (CBR) of compacted soils by using GMDH-type neural network(Springer Heidelberg, 2019) Kurnaz, T. Fikret; Kaya, YilmazThe 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.