In this study, a low-cost, rapid and qualitative evaluation procedure is presented
using dynamic pattern recognition analysis to assess liquefaction potential which is
useful in the planning, zoning, general hazard assessment, and delineation of areas,
Dynamic pattern recognition using neural networks is generally considered to be an
effective tool for assessing of hazard potential on the basis of established criteria. In
this paper, the classification operation, in which an input pattern is passed to the
network and the network produces a representative class as output, is considered for
evaluation of liquefaction hazard potential. The application of Multilayer Artificial
Neural Network for the prediction of liquefaction was examined in the northwest of
Iran (Gilan Plain). The study area suffered acatastrophic earthquake in June 1990 and most of the damage to lifeline facilities and structures in urban areas was brought
about by liquefaction phenomena. The simulated results by multilayer artificial
neural network in this study revealed the high capability of this method to predict the
liquefaction potential of soils