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.

Minimax Probability Machine

2015 ◽  
pp. 182-210
Author(s):  
Pijush Samui ◽  
Yıldırım Huseyin Dalkiliç ◽  
Hariharan Rajadurai ◽  
J. Jagan
Keyword(s):  
Earthquake Engineering ◽  
Cyclic Stress ◽  
Support Vector ◽  
Ground Acceleration ◽  
Cyclic Stress Ratio ◽  
Geotechnical Earthquake Engineering ◽  
Liquefaction Susceptibility ◽  
Minimax Probability Machine ◽  
Input Variables ◽  
Artificial Neural Network Ann

Liquefaction in soil is one of the other major problems in geotechnical earthquake engineering. This chapter adopts Minimax Probability Machine (MPM) for prediction of seismic liquefaction potential of soil based on Shear Wave Velocity (Vs) data. MPM has been used as a classification technique. Two models (MODEL I and MODEL II) have been adopted. In MODEL I, input variables are Cyclic Stress Ratio (CSR), and Vs MODEL II uses Peck Ground Acceleration (PGA) and Vs as input variables. The developed MPM has been compared with the Artificial Neural Network (ANN) and Support Vector Machine (SVM) models. The developed MPM is a robust tool 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

scholarly journals Estimating daily reference evapotranspiration using hybrid gamma test-least square support vector machine, gamma test-ANN, and gamma test-ANFIS models in an arid area of Iran

2018 ◽  
Vol 11 (1) ◽  
pp. 217-240 ◽  
Author(s):  
Akram Seifi ◽  
Hossien Riahi
Keyword(s):  
Support Vector Machine ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Empirical Equations ◽  
Gamma Test ◽  
Hybrid Approaches ◽  
Input Variables ◽  
Predicted Values ◽  
Better Than

Abstract In this study, a hybrid model of least square support vector machine-gamma test (LSSVM-GT) is proposed for estimating daily ETo under arid conditions of Zahedan station, Iran. Gamma test was used for selecting the best input vectors for models. The estimated ETo by LSSVM-GT model with different kernels of RBF, linear and polynomial, were compared with other hybrid approaches including ANN-GT, ANFIS-GT, and empirical equations. The gamma test revealed that climate variables of minimum and maximum air temperature and wind speed are the most important parameters. The LSSVM model performed better than the ANFIS and ANN models when similar meteorological input variables are used. Also, the performance of the three models of LSSVM, ANFIS, and ANN were better than the empirical equations such as Blaney–Criddle and Hargreaves–Samani. The RMSE, MAE, and R2 for the best input vector by LSSVM were 0.1 mm day−1, 0.13 mm day−1, and 0.99, respectively. The threshold of relative absolute error of 95% predicted values by LSSVM, ANN, and ANFIS models were about 8.4%, 9.4%, and 24%, respectively. Based on the comparison of the overall performances, the developed LSSVM-GT approach is greatly capable of providing favorable predictions with high precision in arid regions of Iran.

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