CPT‐Based Liquefaction Evaluation Using Artificial Neural Networks

Abstract

This article presents various artificial neural network (ANN) models for evaluating liquefaction resistance and potential of sandy soils. Various issues concerning ANN modeling such as data preprocessing, training algorithms, and implementation are discussed. The desired ANN is trained and tested with a large historical database of liquefaction performance at sites where cone penetration test (CPT) measurements are available. The ANN models are found to be effective in predicting liquefaction resistance and potential. The developed ANN models are ported to a spreadsheet for ease of use. A simple procedure for conducting uncertainty analysis to address the issue of parameter and model uncertainties is also presented using the ANN‐based spreadsheet model. This uncertainty analysis is carried out using @Risk, which is an add‐in macro that works well with popular spreadsheet programs such as Microsoft Excel and Lotus 1‐2‐3. The results of the present study show that the developed ANN model has potential as a practical design tool for assessing liquefaction resistance of sandy soils.