OCR Prediction Using Support Vector Machine Based on Piezocone Data

Samui, Pijush; Sitharam, T. G.; Kurup, Pradeep

doi:10.1061/(asce)1090-0241(2008)134:6(894)

Cited by 49 publications

(18 citation statements)

References 13 publications

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“…If C goes to infinitely large, SVM would not allow the occurrence of any error and result in a complex model, whereas when C goes to zero, the result would tolerate a large amount of errors and the model would be less complex. The details of SVM and its application to geotechnical engineering problems can be found in literatures (Vapnik 1998;Goh and Goh 2007;Samui 2008;Samui et al 2008;Smola and Scholkopf 2004).…”

Section: Support Vector Machinementioning

confidence: 99%

Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

2010

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This paper describes two artificial intelligence techniques for prediction of maximum dry density (MDD) and unconfined compressive strength (UCS) of cement stabilized soil. The first technique uses various artificial neural network (ANN) models such as Bayesian regularization method (BRNN), Levenberg-Marquardt algorithm (LMNN) and differential evolution algorithm (DENN). The second technique uses the support vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses regression technique by introducing e-insensitive loss function has been adopted. The inputs of both models are liquid limit (LL), plasticity index (PI), clay fraction (CF)%, sand (S)%, gravel Gr (%), moisture content (MC) and cement content (Ce).The sensitivity analyses of the input parameters have been also done for both models. Based on different statistical criteria the SVM models are found to be better than ANN models for the prediction of MDD and UCS of cement stabilized soil.

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Section: Support Vector Machinementioning

confidence: 99%

Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

2010

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“…A comparative study has been carried out between MARS, LSSVM and other traditional methods (Sully et al 1988;Mayne 1991;Chen and Mayne 1994;Tumay et al 1995;Kurup and Dudani 2002;Samui et al 2008) for prediction of OCR. Comparison has been done in terms of root mean square error (RMSE) and mean absolute error (MAE).…”

Section: Resultsmentioning

confidence: 99%

“…However, ANNs have limitations such as being called a black box approach, arriving at local minima, slow convergence speed, over fitting problems and absence of probabilistic output (Park and Rilett 1999;Kecman 2001). Geotechnical engineers successfully employed support vector machine (SVM) to overcome the problems of ANN partly (Pal 2006;Goh and Goh 2007;Samui et al 2008;Das et al 2010). However, the limitations of SVM are given below:…”

Section: Introductionmentioning

confidence: 99%

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

Samui

Kurup

2012

Soft Comput

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This article investigates the feasibility of multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for the prediction of over consolidation ratio (OCR) of clay deposits based on Piezocone Penetration Tests (PCPT) data. MARS uses piece-wise linear segments to describe the non-linear relationships between input and output variables. LSSVM is firmly based on the theory of statistical learning, and uses regression technique. The input parameters of the models are corrected cone resistance (q t ), vertical total stress (r v ), hydrostatic pore pressure (u 0 ), pore pressure at the cone tip (u 1 ), and the pore pressure just above the cone base (u 2 ). The developed LSSVM model gives error bar of predicted OCR. Equations have also been developed for prediction of OCR. The performance of MARS and LSSVM models has been compared with the traditional methods for OCR prediction. As the results reveal, the proposed MARS and LSSVM models are robust models for determination of OCR.

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“…Recently, the Support Vector Machine (SVM) has been proposed for pattern recognition, such as text classification and image recognition (Vapnik, 1995;Ye et al, 2005), and was extended to regression analysis for various applications (Mukherjee et al, 1997;Muller et al, 1997;Samui, 2000;Mita and Hagiwara, 2003;Yu et al, 2006;Zhang et al, 2006;Lee et al, 2007;Samui et al, 2008). In the present study, the support vector machine for regression (support vector regression, SVR) is applied for predicting the stability number of armor blocks of breakwaters.…”

Section: Introductionmentioning

confidence: 99%

Stability number prediction for breakwater armor blocks using Support Vector Regression

Kim

Chang

et al. 2011

KSCE J Civ Eng

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This paper presents the Support Vector Regression (SVR) to predict the stability number of armor blocks of breakwaters. The experimental data of van der Meer are used as the training and test data for the SVR in this study. Estimated results of SVR are compared with those of the empirical formula and a previous Artificial Neural Network (ANN) model. The comparison of results shows the efficiency of the proposed method in the prediction of the stability numbers. The proposed method proves to be an effective tool for designers of rubble mound breakwaters to support their decision process and to improve design efficiency.

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OCR Prediction Using Support Vector Machine Based on Piezocone Data

Cited by 49 publications

References 13 publications

Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

Application of Artificial Intelligence to Maximum Dry Density and Unconfined Compressive Strength of Cement Stabilized Soil

Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction

Stability number prediction for breakwater armor blocks using Support Vector Regression

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