Samui, P and Sitharam, TG (2011) Machine learning modelling for predicting soil liquefaction susceptibility. In: Natural Hazards and Earth System Sciences, 11 (1). pp. 1-9.Full text not available from this repository. (Request a copy)
This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Copernicus Group.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||26 Apr 2011 08:19|
|Last Modified:||26 Apr 2011 08:19|
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