Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Multivariate adaptive regression spline applied to friction capacity of driven piles in clay

  • Samui, Pijush (Centre for Disaster Mitigation and Management, VIT University)
  • Received : 2011.09.12
  • Accepted : 2011.11.22
  • Published : 2011.12.25
⟨ Previous Next ⟩

Abstract

This article employs Multivariate Adaptive Regression Spline (MARS) for determination of friction capacity of driven piles in clay. MARS is non-parametric adaptive regression procedure. Pile length, pile diameter, effective vertical stress, and undrained shear strength are considered as input of MARS and the output of MARS is friction capacity. The developed MARS gives an equation for determination of $f_s$ of driven piles in clay. The results of the developed MARS have been compared with the Artificial Neural Network. This study shows that the developed MARS is a robust model for prediction of $f_s$ of driven piles in clay.

Keywords

References

  1. Akin, S. and Karpuz, C. (2008), "Estimating drilling parameters for diamond bit drilling operations using articial neural networks", Int. J. Geomech., 8(1), 68-73. https://doi.org/10.1061/(ASCE)1532-3641(2008)8:1(68)
  2. Andres, J.D., Lorca, P., Juez, F.J.D.C. and Lasheras, F.S. (2011), "Bankruptcy forecasting: a hybrid approach using fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS)", Expert Syst. Appl., 38,1866-1875. https://doi.org/10.1016/j.eswa.2010.07.117
  3. Attoh-Okine, N.O., Cooger, K. and Mensah, S. (2009), "Multivariate adaptive regression (MARS) and hinged hyperplanes (HHP) for doweled pavement performance modeling", Constr. Build. Mater., 23(9), 3020-3023. https://doi.org/10.1016/j.conbuildmat.2009.04.010
  4. Burland, J.B. (1973), "Shaft friction of piles in clay-a simple fundamental approach", Ground Eng., 6(3), 30-42.
  5. Chandler, R.J. (1968), "The shaft friction of piles in cohesive soil in terms of effective stress", Civil Eng. Public Works Rev., 63, 48-51.
  6. Crino, S. and Brown, D.E. (2007), "Global optimization with multivariate adaptive regression splines", IEEE T. Syst. Man Cy. B, 37(2), 333-340. https://doi.org/10.1109/TSMCB.2006.883430
  7. Deconinck, E., Coomans, D. and Heyden, V.Y. (2007), "Exploration of linear modelling techniques and their combination with multivariate adaptive regression splines to predict gastro-intestinal absorption of drugs", J. Pharmaceut. Biomed., 43,119-130. https://doi.org/10.1016/j.jpba.2006.06.022
  8. Ekman, T. and Kubin, G. (1999), "Nonlinear prediction of mobile radio channels: measurements and mars model designs", IEEE International Conference on Acoustics, Speech, and Signal Processing, 5, 2667-2670.
  9. Friedman, J.H. (1991), "Multivariate adaptive regression splines", Ann Stat., 19, 1-141. https://doi.org/10.1214/aos/1176347963
  10. Friedman, J.H. and Roosen, C.B. (1995), "An introduction to multivariate adaptive regression splines", Stat. Methods Med. Res., 4, 197-217. https://doi.org/10.1177/096228029500400303
  11. Goh, A.T.C. (1995), "Empirical design in geotechnics using neural networks", Geotechnique, 45(4), 709-714. https://doi.org/10.1680/geot.1995.45.4.709
  12. Hastie, T., Tibshirani, R. and Friedman, J.H. (2003), The elements of statistical learning, Springer-Verlag, New York.
  13. Kecman, V. (2001), Leaming and soft computing: support vector machines, neural networks, and fuzzy logic models, The MIT press, Cambridge, Massachusetts, London, England.
  14. Lewis, P.A.W. and Stevens, J.G. (1991), "Nonlinear modeling of time series using multivariate adaptive regression splines (mars)", J. Am. Stat. Assoc., 86(416), 864-877. https://doi.org/10.1080/01621459.1991.10475126
  15. Mayoraz, F. and Vulliet, L. (2002), "Neural networks for slope movement prediction", Int. J. Geomech., 2, 153-173. https://doi.org/10.1061/(ASCE)1532-3641(2002)2:2(153)
  16. McClelland, B. (1972), "Design and performance of deep foundations in clay", General Report. Am. Sot. Civ. Engrs Specialty Conf Performance of Earth and Earth-supported Structures, 2, 111-114.
  17. Meyerhof, G.G. (1976), "Bearing capacity and settlement of pile foundations", J. Geotech. Eng. Div. -ASCE, 102(GT3), 195-228.
  18. Miranda, T., Correia, A.G., Santos, M., Sousa, L.R. and Cortez, P. (2011), "New models for strength and deformability parameter calculation in rock masses using data-mining techniques", Int. J. Geomech., 11(1), 44-58. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000071
  19. Park, D. and Rilett, L.R. (1999), "Forecasting freeway link ravel times with a multi-layer feed forward neural network", Comput. Aided Civil Infrastruct. Eng., 14, 358-367.
  20. Parry, R.H.G. and Swain, C.W. (1977a), "Effective stress methods of calculating skin friction on driven piles in soft clay", Ground Eng., 10(3), 24-26.
  21. Parry, R.H.G. and Swain, C.W. (1977b), "A study of skin friction on piles in stiff clay", Ground Eng., 10(8), 33-37.
  22. Randolph, M.F., Carter, J.P. and Wroth, C.P. (1979), "Driven piles in clay-the effects of installation and subsequent consolidation", Geotechnique, 29(4), 361-393. https://doi.org/10.1680/geot.1979.29.4.361
  23. Samui, P. and Sitharam, T.G. (2010), "Site characterization model using articial neural network and kriging", Int. J. Geomech., 10(5), 171-180. https://doi.org/10.1061/(ASCE)1532-3641(2010)10:5(171)
  24. Sekulic, S. and Kowalski, B.R. (1992), "MARS: a tutorial", J. Chemometr., 6, 199-216. https://doi.org/10.1002/cem.1180060405
  25. Tomlinson, M.J. (1971), Some effects of pile driving on skin friction, behaviour of piles, Institution of Civil Engineers, London, 107-I 14.
  26. Vidoli, F. (2011), "Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splines", Eur. J. Oper. Res., 212, 583-595. https://doi.org/10.1016/j.ejor.2011.02.003
  27. Yang, C.C., Prasher, S.O., Lacroix, R. and Kim, S.H. (2004), "Application of multivariate adaptive regression splines (mars) to simulate soil temperature", Trans. ASAE, 47(3), 881-887. https://doi.org/10.13031/2013.16085
  28. Zaman, M., Solanki, P., Ebrahimi, A. and White, L. (2010), "Neural network modeling of resilient modulus using routine subgrade soil properties", Int. J. Geomech., 10(1), 1-12. https://doi.org/10.1061/(ASCE)1532-3641(2010)10:1(1)
  29. Zhang, G., Xiang, X. and Tang, H. (2011), "Time series prediction of chimney foundation settlement by neural networks", Int. J. Geomech., 11(3), 154-158. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000029

Cited by

  1. Lateral Load Capacity of Piles in Clay Using Genetic Programming and Multivariate Adaptive Regression Spline vol.45, pp.3, 2015, https://doi.org/10.1007/s40098-014-0142-2
  2. Estimation of scour depth below free overfall spillways using multivariate adaptive regression splines and artificial neural networks vol.9, pp.1, 2015, https://doi.org/10.1080/19942060.2015.1011826
  3. Artificial intelligence design charts for predicting friction capacity of driven pile in clay pp.1433-3058, 2019, https://doi.org/10.1007/s00521-018-3555-5
  4. Assessment of slope stability using multiple regression analysis vol.13, pp.2, 2011, https://doi.org/10.12989/gae.2017.13.2.237
  5. Determination of Young Elasticity Modulus in Bored Piles Through the Global Strain Extensometer Sensors and Real-Time Monitoring Data vol.9, pp.15, 2011, https://doi.org/10.3390/app9153060
  6. An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils vol.21, pp.6, 2011, https://doi.org/10.12989/gae.2020.21.6.583