Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Nat. Hazards Earth Syst. Sci., 11, 1-9, 2011
https://doi.org/10.5194/nhess-11-1-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
03 Jan 2011
Machine learning modelling for predicting soil liquefaction susceptibility
P. Samui1 and T. G. Sitharam2 1Centre for Disaster Mitigation and Management, VIT University, Vellore – 632014, India
2Department of Civil Engineering, Indian Institute of Science, Bangalore – 560012, India
Abstract. 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 [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/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.

Citation: Samui, P. and Sitharam, T. G.: Machine learning modelling for predicting soil liquefaction susceptibility, Nat. Hazards Earth Syst. Sci., 11, 1-9, https://doi.org/10.5194/nhess-11-1-2011, 2011.
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