A rational method for development of limit state for liquefaction evaluation based on shear wave velocity measurements

This papers presents a new approach for developing a limit state for liquefaction evaluation based on field performance data. As an example to illustrate the new approach, a database that consists of, among many other features, in situ shear wave velocity measurements and field observations of liquefaction/non-liquefaction in historic earthquakes is analysed. This database is first used to train a neural network to classify liquefaction/non-liquefaction based on soil resistance parameters and load parameters. The successfully trained and tested neural network is then used to establish a limit state: a multiple dimension boundary that separates 'zone' of liquefaction from 'zone' of non-liquefaction. The limit state yields cyclic resistance ratio for a given set of soil resistance parameters. Examination of all cases in the database show that the developed limit state has a high degree of accuracy in predicting the occurrence of liquefaction/non-liquefaction. The developed neural network model can accurately predict the cyclic resistance ratio of soils. Copyright (C) 2000 John Wiley & Sons, Ltd.