EVALUATION OF LIQUEFACTION POTENTIAL OF SOIL BASED ON SHEAR WAVE VELOCITY USING EXTREME LEARNING MACHINE
Soil liquefaction is one of the main causes for the heavy damages to the infrastructures and lifeline systems caused by the earthquakes and its identification is first step towards mitigation. In recent past soft computing techniques such as artificial neural network (ANN) support vector machine (SVM) and genetic programming (GP) are found to be more efficient compared to statistical methods. In the present study, relatively new soft computing technique known as extreme learning machine (ELM) is used to predict the liquefaction susceptibility of soil based on shear wave velocity (Vs) measurement data. The ELM is only suitable for single hidden layer back propagation neural network (BPNN) in which the input weights and hidden layer biases are chosen arbitrarily but, the hidden layer output weights and biases are determined analytically by inverse operation of hidden layer output matrices. As the parameters are obtained analytically, chances of solution getting stuck in local minima is less, hence the generalization of the model improves. Based on the obtained model parameters empirical equation is presented, which can be used by the geotechnical professionals for prediction of liquefaction potential of a soil in a future seismic event. The performance of the developed model is compared with that of other available soft computing model in terms of rate of successful prediction.