Estimation of Shear Wave Velocity Using Sugeno Fuzzy Logic and Artificial Neural Networks Models

It is quite hard to determine the dynamic properties of the soils, where there is no or limited data on seismic experiments. Most of the recent research uses the previous experimental results obtained from the similar soil profiles to estimate the dynamic soil properties for the existent site to be analyzed. Hence, it is possible to come across numerous empirical relations and correlations that were developed for these dynamic properties and estimations in the literature. This study focuses on the analysis of 44 Seismic Cone Penetration tests and 64 SPT tests performed on a Kuvaterner Alluvial soil profile in 33 different measurement locations in city of Adapazari with different models. The relations between SPT-N values and shear wave velocity values measured from the seismic cone penetration tests are evaluated and a model study based on this relation is assessed. Artificial neural network and Sugeno-Fuzzy Logic models are adopted for the estimation of dynamic properties. Evaluations of the models based on probabilistic approaches using correlation coefficient, average absolute error and mean squared error have revealed that the Sugeno-Fuzzy Logic model was superior to artificial neural network model. This soil model performs well to estimate the dynamic properties of soil profiles in Adapazari or similar soil profiles where the seismic tests were not performed at all. The model also proposes a useful tool to cross-check the shear wave velocity results from the seismic tests and to complement the site investigation for the cases where there is limited data.