Geomechanics and Engineering

  • 제1권3호
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  • Pages.259-262
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  • 2009
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Pullout capacity of small ground anchors: a relevance vector machine approach

  • Samui, Pijush (Department of Civil Engineering, Tampere University of Technology) ;
  • Sitharam, T.G. (Department of Civil Engineering, Indian Institute of Science)
  • 투고 : 2008.10.27
  • 심사 : 2009.07.28
  • 발행 : 2009.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper examines the potential of relevance vector machine (RVM) in prediction of pullout capacity of small ground anchors. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network (ANN) model. Overall, the RVM showed good performance and is proven to be better than ANN model. It also estimates the prediction variance. The plausibility of RVM technique is shown by its superior performance in forecasting pullout capacity of small ground anchors providing exogenous knowledge.

키워드

참고문헌

  1. Das, S.K. and Basudhar, P.K. (2006), "Undrained lateral load capacity of piles in clay using artificial neural network", Comput. Geotech., 33(8), 454-459. https://doi.org/10.1016/j.compgeo.2006.08.006
  2. Figueiredo, M.A.T. (2003), "Adaptive sparseness for supervised learning", IEEE T. Pattern Anal. Mach. Intel., 25(9), 1150.
  3. Shahin, M.A. and Jaksa, M.B. (2006), "Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks", Can. Geotech. J., 43, 626-637. https://doi.org/10.1139/t06-029
  4. Tipping, M.E. (2001), "Sparse Bayesian learning and the relevance vector machine", J. Mach. Learn, 1, 211-244.

피인용 문헌

  1. Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques vol.2017, 2017, https://doi.org/10.1155/2017/2601063
  2. Model studies of uplift capacity behavior of square plate anchors in geogrid-reinforced sand vol.8, pp.4, 2015, https://doi.org/10.12989/gae.2015.8.4.595