Predicting potential of controlled blasting-induced liquefaction using neural networks and neuro -fuzzy system

In recent years, controlled blasting has turned into an efficient method for evaluation of soil liquefaction in real scale and evaluation of ground improvement techniques. Predicting blast-induced soil liquefaction by using collected information can be an effective step in the[a1] study of blast-induced liquefaction. In this study, to estimate residual pore pressure ratio, first, multi- layer perceptron neural network is used in which error (RMS) for the network was calculated as 0.105. Next, neuro-fuzzy network, ANFIS was used for modeling. Different ANFIS models are created using Grid partitioning (GP), Subtractive Clustering (SCM), and Fuzzy C-means Clustering (FCM). Minimum error is obtained using by FCM at about 0.081. Finally, radial basis function (RBF) network is used. Error of this method was about 0.06. Accordingly, RBF network has better performance. Variables including fine-content, relative density, effective overburden pressure and SPT value are considered as input components and the Ru, residual[a2] pore pressure ratio was used as the only output component for designing prediction models. In the next stage the network output is compared with the results of a regression analysis. Finally, sensitivity analysis for RBF network is tested, its results reveal that and SPT are the most effective factors in determining Ru.