APPRAISAL OF LIQUEFACTION POTENTIAL USING NEURAL NETWORK AND NEURO FUZZY APPROACH

In this study, standard penetration test dependent bore-log charts of different boreholes were collected for selected locations in order to prepare the datasets. Datasets were applied to the Idriss and Boulanger method to evaluate liquefaction potential. Complete datasets were used for development of neural network and neuro-fuzzy models. Feed forward backpropagation algorithm with a multilayer perceptron network is utilized to analyze the liquefaction occurrence in different locations. To meet the objective, 159 sets of geotechnical data were collected, out of which 133 datasets were used for development of models and 26 datasets were used for validation. Neural network models were trained with six input vectors by optimum numbers of hidden layers, epoch, and suitable transfer functions. Neuro-fuzzy models have been developed using the Takagi-Sugeno-Kang reliant approach. The predicted values of liquefaction potential by artificial neural networks and neuro-fuzzy models were compared with an empirical method (i.e., Idriss and Boulanger method). The compared values of liquefaction potential obtained by neural network and neuro-fuzzy models shows that trained artificial neural network models' prediction capability are better than that of neuro-fuzzy models.