CLC number: TU74
On-line Access: 2015-04-03
Received: 2012-10-10
Revision Accepted: 2013-05-14
Crosschecked: 2015-02-25
Cited: 4
Clicked: 7311
Pijush Samui, Dookie Kim, Bhairevi G. Aiyer. Pullout capacity of small ground anchor: a least square support vector machine approach[J]. Journal of Zhejiang University Science A, 2015, 16(4): 295-301.
@article{title="Pullout capacity of small ground anchor: a least square support vector machine approach",
author="Pijush Samui, Dookie Kim, Bhairevi G. Aiyer",
journal="Journal of Zhejiang University Science A",
volume="16",
number="4",
pages="295-301",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1200260"
}
%0 Journal Article
%T Pullout capacity of small ground anchor: a least square support vector machine approach
%A Pijush Samui
%A Dookie Kim
%A Bhairevi G. Aiyer
%J Journal of Zhejiang University SCIENCE A
%V 16
%N 4
%P 295-301
%@ 1673-565X
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1200260
TY - JOUR
T1 - Pullout capacity of small ground anchor: a least square support vector machine approach
A1 - Pijush Samui
A1 - Dookie Kim
A1 - Bhairevi G. Aiyer
J0 - Journal of Zhejiang University Science A
VL - 16
IS - 4
SP - 295
EP - 301
%@ 1673-565X
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1200260
Abstract: This study employs the least square support vector machine (LSSVM) for the prediction of pullout capacity of small ground anchor. LSSVM is firmly based on the theory of statistical learning and uses regression technique. In LSSVM, Vapnik and Lerner (1963)’s ε-insensitive loss function was replaced by a cost function which corresponded to a form of ridge regression. The input parameters of LSSVM were equivalent anchor diameter, anchor embedment depth, average cone tip resistance, average cone sleeve friction, and installation technique. Using 83 out the available 119 In-situ test datasets, an LSSVM regression model was developed. The goodness of the model was tested using the remaining 36 data points. The developed LSSVM also gave an error bar of predicted data. A sensitivity analysis was conducted to determine the effect of each input parameter. The results were compared with the artificial neural network (ANN) model. Overall, LSSVM was shown to perform well.
In this paper, the authors employed Least Support Vector Machine (LSSVM) for the prediction of pullout capacity of small ground anchor. They used 119 in-situ datasets while developing the LSSVM model. They compared the LSSVM results with the in-situ test results and used different performance indices (R, MAE, and RMSE) to check the performance of the LSSVM model. Additionally, they performed sensitivity analyses to determine the effect of each input parameter. Finally, they compared the LSSVM results with the results obtained from the ANN model developed by Shahin and Jaksa (2006) for testing samples.
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