Evaluation of liquefaction potential using neural-networks and CPT results

In this research, a reliable Cone Penetration Test data set was gathered with a wide range of parameters. This data was incorporated in a Neural-Networks computer software called STATISTICA Neural-Networks. The back propagation algorithm with a multilayer perceptron network is utilized to analyze the liquefaction occurrence in different sites. In this study, different sets of effective parameters for the neural-network analyses are selected such that to reduce the noise and to obtain more accurate results. Considering the relative importance of effective parameters in liquefaction assessment, it is indicated that sigma(0), sigma(0)(1) together play a more important role than what previously was assumed and hence the relative importance of the q(c) and seismic parameters are decreased compared with the previous works. The results presented here have more accuracy than previous works while at the same time, the range of the parameters used in this study is much wider than what was previously used. This range of parameters makes the proposed method applicable for practical purposes. (C) 2003 Elsevier Ltd. All rights reserved.