Samui, Pijush and Bhattacharya, Gautam and Choudhury, Deepankar (2009) Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach. In: 12th Online World Conference on Soft Computing in Industrial Applications (WFSC 12), OCT 16-26, 2007, England.
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This study investigates the potential of Relevance Vector Machine (RVM)-based approach to predict the ultimate capacity of laterally loaded pile in clay. RVM is a sparse approximate Bayesian kernel method. It can be seen as a probabilistic version of support vector machine. It provides much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. RVM model outperforms the two other models based on root-mean-square-error (RMSE) and mean-absolute-error (MAE) performance criteria. It also stimates the prediction variance. The results presented in this paper clearly highlight that the RVM is a robust tool for prediction Of ultimate capacity of laterally loaded piles in clay.
|Item Type:||Conference Paper|
|Additional Information:||Copyright of this publications belongs to Springer.|
|Keywords:||pile;clay;relevance vector machine;ultimate capacity.|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||09 Feb 2010 11:23|
|Last Modified:||19 Sep 2010 05:31|
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