Determination of liquefaction susceptibility of soil: a least square support vector machine approach

2012 ◽  
Vol 37 (9) ◽  
pp. 1154-1161 ◽  
Author(s):  
Pijush Samui ◽  
J. Karthikeyan
Keyword(s):  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Liquefaction Susceptibility

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2016 ◽  
pp. 124-160 ◽  
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils

2018 ◽  
pp. 1507-1543
Author(s):  
J. Jagan ◽  
Prabhakar Gundlapalli ◽  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Earthquake Engineering ◽  
Relevance Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Ground Acceleration ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Input Variables

The determination of liquefaction susceptibility of soil is a paramount project in geotechnical earthquake engineering. This chapter adopts Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Least Square Support Vector Machine (LSSVM) for determination of liquefaction susceptibility based on Cone Penetration Test (CPT) from Chi-Chi earthquake. Input variables of SVM, RVM and LSSVM are Cone Resistance (qc) and Peak Ground Acceleration (amax/g). SVM, RVM and LSSVM have been used as classification tools. The developed SVM, RVM and LSSVM give equations for determination of liquefaction susceptibility of soil. The comparison between the developed models has been carried out. The results show that SVM, RVM and LSSVM are the robust models for determination of liquefaction susceptibility of soil.

State-of-the-Art Least Square Support Vector Machine Application for Accurate Determination of Natural Gas Viscosity

2013 ◽  
Vol 53 (2) ◽  
pp. 945-958 ◽  
Author(s):  
Amir Fayazi ◽  
Milad Arabloo ◽  
Amin Shokrollahi ◽  
Mohammad Hadi Zargari ◽  
Mohammad Hossein Ghazanfari
Keyword(s):  
Support Vector Machine ◽  
Natural Gas ◽  
State Of The Art ◽  
Accurate Determination ◽  
Least Square ◽  
Support Vector ◽  
Gas Viscosity

Lateral Displacement of Liquefaction Induced Ground Using Least Square Support Vector Machine

2011 ◽  
Vol 2 (2) ◽  
pp. 29-39 ◽  
Author(s):  
Sarat Kumar Das ◽  
Pijush Samui ◽  
Dookie Kim ◽  
N. Sivakugan ◽  
Rajanikanta Biswal
Keyword(s):  
Support Vector Machine ◽  
Model Equation ◽  
Lateral Displacement ◽  
Least Square ◽  
Disaster Mitigation ◽  
Support Vector ◽  
Model Parameters ◽  
Ann Models ◽  
Artificial Neural Network Ann

The determination of lateral displacement of liquefaction induced ground during an earthquake is an imperative task in disaster mitigation. This study investigates the possibility of using least square support vector machine (LSSVM) for the prediction of lateral displacement of liquefaction induced ground during an earthquake. The results have been compared with those obtained using artificial neural network (ANN) models and observed that LSSVM outperformed the ANN models. Model equation has been presented based on the model parameters, which can be used by the professionals. Sensitivity analysis has also been performed to determine the importance of each of the input parameters.

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