Use of Artificial Neural Networks in the Prediction of Liquefaction Resistance of Sands
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 132, Issue 11
Abstract
A backpropagation artificial neural network (ANN) model has been developed to predict the liquefaction cyclic resistance ratio (CRR) of sands using data from several laboratory studies involving undrained cyclic triaxial and cyclic simple shear testing. The model was verified using data that was not used for training as well as a set of independent data available from laboratory cyclic shear tests on another soil. The observed agreement between the predictions and the measured CRR values indicate that the model is capable of effectively capturing the liquefaction resistance of a number of sands under varying initial conditions. The predicted CRR values are mostly sensitive to the variations in relative density thus confirming the ability of the model to mimic the dominant dependence of liquefaction susceptibility on soil density already known from field and experimental observations. Although it is common to use mechanics-based approaches to understand fundamental soil response, the results clearly demonstrate that non-mechanistic ANN modeling also has a strong potential in the prediction of complex phenomena such as liquefaction resistance.
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Information
Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 132 • Issue 11 • November 2006
Pages: 1502 - 1504
Copyright
© 2006 ASCE.
History
Received: Jan 13, 2003
Accepted: Jul 18, 2005
Published online: Nov 1, 2006
Published in print: Nov 2006
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