Yazar "Kurnaz, Talas Fikret" seçeneğine göre listele

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    Öğe
    SPT-based liquefaction assessment with a novel ensemble model based on GMDH-type neural network
    (Springer Heidelberg, 2019) Kurnaz, Talas Fikret; Kaya, Yilmaz
    Liquefaction is one of the most complex problems in geotechnical earthquake engineering. This paper proposes a novel ensemble group method of data handling (EGMDH) model based on classification for the prediction of liquefaction potential of soils. The database used in this study consists of 451 standard penetration test (SPT)-based case records from two major earthquakes. The input parameters are selected as SPT blow numbers, percent finest content less than 75 mu m, depth of groundwater table, total and effective overburden stresses, maximum peak ground acceleration, and magnitude of earthquake for the prediction models. The proposed EGMDH model results were also compared with other classifier models, particularly the results of the GMDH model. The results of this study indicated that the proposed EGMDH model has achieved more successful results on predicting the liquefaction potential of soils compared with the other classifier models by improving the prediction performance of GMDH model.
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    Öğe
    The comparison of the performance of ELM, BRNN, and SVM methods for the prediction of compression index of clays
    (Springer Heidelberg, 2018) Kurnaz, Talas Fikret; Kaya, Yilmaz
    The compression index (Cc) is a necessary parameter for the settlement calculation of clays. However, determination of the compression index from oedometer tests takes a relatively long time and leads to a very demanding experimental working program in the laboratory. Therefore, geotechnical engineering literature involves many studies based on indirect methods such as multiple regression analysis (MLR) and soft computing methods to determine the compression index. This study is aimed to predict the compression index by using extreme learning machine (ELM), Bayesian regularization neural network (BRNN), and support vector machine (SVM) methods. The selected variables for each method are the natural water content (w(n)), initial void ratio (e(0)), liquid limit (LL), and plasticity index (PI) of clay samples. Many trials were carried out in order to get the best prediction performance with each model. The application results obtained from the models were also compared based on the correlation coefficient (R), coefficient of efficiency (E), and mean squared error (MSE). The results indicate that the BRNN method has better success on estimation of the compression index compared to the ELM and SVM methods.
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    Öğe
    The performance comparison of the soft computing methods on the prediction of soil compaction parameters
    (Springer Heidelberg, 2020) Kurnaz, Talas Fikret; Kaya, Yilmaz
    The compaction parameters of soils known as the optimum moisture content (OMC) and maximum dry density (MDD) are necessary for the geotechnical engineering applications such as the fills, embankments, and dams. However, it takes a long time to determine the compaction parameters due to the laboratory test procedure. It was aimed to estimate the compaction parameters of soils with four soft computing methods and also to compare the performance of the methods in this study. For this purpose, a wide database consisting the index and standard proctor (SP) test results were used. Although all AI methods used in this study are successful on estimation of the MDD and OMC parameters, it was seen that the ELM method was the most successful method on the prediction of compaction parameters.