Evaluation of case-based reasoning to estimate liquefaction manifestation

Abstract

This article develops a framework for and explores the use of case-based reasoning (CBR) to predict seismically induced liquefaction manifestation. CBR is an artificial intelligence process that solves new problems using the known answers to similar past problems. CBR sorts a database of case histories based on their similarity to a design case and predicts the outcome of the design case as the observed outcome of the most similar case history or majority outcome of the most similar case histories. Two databases of liquefaction case histories are used to develop and validate numerous CBR models. Different input parameters and aspects of the CBR method and their influence on the predictive capability of the models are evaluated. Some of the developed CBR models were shown to have a better predictive power than currently existing models. However, more research is needed to refine these models before they can be used in practice. Nevertheless, this study shows the potential of CBR as a method to estimate liquefaction manifestation and suggests several avenues of future research.