Abstract
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 estimates 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.
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Samui, P., Bhattacharya, G., Choudhury, D. (2009). Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach. In: Avineri, E., Köppen, M., Dahal, K., Sunitiyoso, Y., Roy, R. (eds) Applications of Soft Computing. Advances in Soft Computing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88079-0_13
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DOI: https://doi.org/10.1007/978-3-540-88079-0_13
Publisher Name: Springer, Berlin, Heidelberg
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