Samui, Pijush (2008) Prediction of friction capacity of driven piles in clay using the support vector machine. In: Canadian Geotechnical Journal, 45 (2). pp. 288-295.
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The support vector machine (SVM) is an emerging machine learning technique where prediction error and model complexity are simultaneously minimized. This paper examines the potential of SVM to predict the friction capacity of driven piles in clay. This SVM is firmly based on the statistical learning theory and uses the regression technique by introducing accuracy $(\varepsilon)$ insensitive $\varepsilon$loss function. The results are compared with those from a widely used artificial neural network (ANN) model. Overall, the SVM showed good performance and is proven to be better than ANN model. A sensitivity analysis has been also performed to investigate the importance of the input parameters. The study shows that SVM has the potential to be a useful and practical tool for prediction of friction capacity of driven piles in clay.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to National Research Council, Canada.|
|Keywords:||piles;clay;artificial neural network;support vector machine.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||05 May 2008|
|Last Modified:||19 Sep 2010 04:44|
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