Skip to main content
SpringerLink
Log in

Prediction of Ultimate Capacity of Laterally Loaded Piles in Clay: A Relevance Vector Machine Approach

  • Conference paper
Book cover Applications of Soft Computing

Part of the book series: Advances in Soft Computing ((AINSC,volume 52))

  • 784 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Czerniak, E.: Resistance to overturning of single, short piles. J. Structural Engg. 83(ST2), 1–25 (1957)

    Google Scholar 

  2. Hansen, B.J.: The ultimate resistance of rigid piles against transversal forces. Geo-tekenish institute Bull. No.12, Copenhagen (1961)

    Google Scholar 

  3. Broms, B.B.: Lateral resistance of pile in cohesive soils. J. Soil Mech. Found. Div. 90(SM2), 27–63 (1964)

    Google Scholar 

  4. Meyerhof, G.G.: The bearing capacity of rigid piles and pile groups under inclined loads in clay. Canadian Geotechnical Journal 18, 297–300 (1981)

    Article  Google Scholar 

  5. Chow, Y.K., Chan, L.F., Liu, L.F., Lee, S.L.: Prediction of pile capacity from sress-wave measurements: A neural network approach. International Journal of Numerical and Analytical Methods in Geomechanics 19, 107–126 (1995)

    Article  MATH  Google Scholar 

  6. Lee, I.M., Lee, J.H.: Prediction of pile bearing capacity using artificial neural network. Computers and Geotechnics 18(3), 189–200 (1996)

    Article  Google Scholar 

  7. Abu Kiefa, M.A.: General Regression Neural Networks for driven piles in cohe-sionless soils. Journal of Geotechnical and Geoenviromental Engineering 124(12), 1177–1185 (1998)

    Article  Google Scholar 

  8. Nawari, N.O., Liang, R., Nusairat, J.: Artificial Intelligence Techniques for the Design and Analysis of Deep Foundations. Electronics Journal of Geotechnical Engineering (1999)

    Google Scholar 

  9. Park, D., Rilett, L.R.: Forecasting freeway link ravel times with a multi-layer feed for-ward neural network. Computer Aided Civil And infa Structure Engineering 14, 358–367 (1999)

    Google Scholar 

  10. Kecman, V.: Leaning And Soft Computing: Support Vector Machines. In: Neural Net-works, And Fuzzy Logic Models. MIT press, Cambridge (2001)

    Google Scholar 

  11. Rao, K.M., Suresh Kumar, V.: Measured and predicted response of laterally loaded piles. In: Proc. 6th International Conference and Exhibition on Piling and Deep Foundations, Bombay, pp. 1.6.1–1.6.7. Deep Foundation Institute (1996)

    Google Scholar 

  12. Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. 1, 211–244 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  13. Tipping, M.E.: The relevance vector machine. Adv. Neural Inf. Process. Syst. 12, 625–658 (2000)

    Google Scholar 

  14. Berger, J.O.: Statistical Decision Theory and Bayesian Analysis, 2nd edn. Springer, New York (1985)

    MATH  Google Scholar 

  15. Wahaba, G.: A comparison of GCV and GML for choosing the smoothing parameters in the generalized spline-smoothing problem. Ann. Stat. 4, 1378–1402 (1985)

    Article  Google Scholar 

  16. MacKay, D.J.: Bayesian methods for adaptive models. Ph.D. thesis, Dep. of Comput. and Neural Sysyt., Calif Inst. of Technol., Pasadena. Calif. (1992)

    Google Scholar 

  17. Sincero, A.P.: Predicting Mixing Power Using Artificial Neural Network. EWRI World Water and Environmental (2003)

    Google Scholar 

  18. MathWork Inc, Matlab user’s manual, Version 5.3. Natick, MA, The MathWorks, Inc. (1999)

    Google Scholar 

  19. Li, Y., Campbell, C., Tipping, M.: Bayesian automatic relevance determination algorithms for classifying gene expression data. Bioinformatics 18(10), 1332–1339 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Scholar, Department of Civil Engineering, Indian Institute of Science, Bangalore, 560 012, India

    Pijush Samui

  2. Professor, Deparment of Civil Engineering, Bengal Engineering and Science University, Howrah, 711103, India

    Gautam Bhattacharya

  3. Associate Professor, Department of Civil Engineering, Indian Institute of Technology, Bombay, Mumbai, 400076, India

    Deepankar Choudhury

Authors
  1. Pijush Samui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gautam Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Deepankar Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Senior Lecturer in Integrated Transport Centre for Transport & Society Faculty of the Built Environment, University of the West of England, Frenchay Campus Coldharbour Lane, BS16 1QY, Bristol, UK

    Erel Avineri

  2. Kyushu Institute of Technology, Dept. Artificial Intelligence, 680-4 Kawazu Izuka-Shi, 820-8502, Fukuoka, Japan

    Mario Köppen

  3. MOSAIC Research Group Department of Computing, University of Bradford, BD7 1DP, Bradford, UK

    Keshav Dahal

  4. Centre for Transport & Society Faculty of the Built Environment, University of the West of England, Frenchay Campus Coldharbour Lane, BS16 1QY, Bristol, UK

    Yos Sunitiyoso

  5. Professor of Decision Engineering and Head of Decision Engineering Centre Manufacturing Department, Cranfield University, MK43 OAL, Bedford, UK

    Rajkumar Roy

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88079-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88078-3

  • Online ISBN: 978-3-540-88079-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation