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Artificial intelligence design charts for predicting friction capacity of driven pile in clay

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Abstract

In this study, five nonlinear prediction tools are used to model and predict the friction capacity of driven piles installed in clay including classical support vector machine (SVM) and two of its variants, namely regularized generalized proximal SVM and twin SVM, adaptive neuro-fuzzy inference system (ANFIS) and genetic programming (GP). The undrained shear strength, effective vertical stress, pile diameter and pile length are taken as the input parameters of the developed models, and the friction capacity is considered as the output. A total of 80 experimental observations are collected and used to train and test several models estimating the friction capacity of a driven pile in clay. The results demonstrate that, noting the root-mean-square error (RMSE) value, for prediction of the friction capacity of driven piles in clay the ANFIS model gives a better convergence to the in situ results, compared with the GP and SVM models. The developed ANFIS model provided a simple and reliable design structure for proper selection of the pile friction capacity of driven piles installed in clay. Furthermore, a simple mathematical formula is presented based on the GP model. The predicted results are compared with in situ data set models to demonstrate the abilities of the AI models. In order to perform a model evaluation, in addition to RMSE, the regression coefficient of determination is obtained through testing and training of the SVM, ANFIS and GP models. The results show high reliability for the developed models. The presented ANFIS and GP models are introduced as new models in the field of geotechnical engineering.

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References

  1. Esrig MI, Kirby RC, Murphy BS (1981) Advances in general effective stress method for prediction of axial capacity for driven piles in clay. J Pet Technol 33(9):1793–1802

    Article  Google Scholar 

  2. McVay MC, Birgisson B, Zhang LM, Perez A, Putcha S (2000) Load and resistance factor design (LRFD) for driven piles using dynamic methods—a Florida perspective. Geotech Test J 23(1):55–66

    Article  Google Scholar 

  3. Yang L, Liang R (2006) Incorporating set-up into reliability-based design of driven piles in clay. Can Geotech J 43(9):946–955

    Article  Google Scholar 

  4. Seo H, Yildirim IZ, Prezzi M (2009) Assessment of the axial load response of an H pile driven in multilayered soil. J Geotech Geoenviron Eng 135(12):1789–1804

    Article  Google Scholar 

  5. Kim D, Lee J (2012) Resistance factor contour plot analyses of load and resistance factor design of axially-loaded driven piles in clays. Comput Geotech 44:9–19

    Article  Google Scholar 

  6. Fattah MY, Al-Mosawi MJ, Al-Zayadi AAO (2013) Time dependent behavior of piled raft foundation in clayey soil. Geomech Eng 5(1):17–36

    Article  Google Scholar 

  7. Afshin A, Rayhani MT (2015) Evaluation of bearing capacity with time for small-scale piles driven into Leda clay. Int J Geotech Eng 9(3):307–315

    Article  Google Scholar 

  8. Saye SR, Lutenegger AJ, Brown DA, Kumm BP (2016) Influence of sample disturbance on estimated side resistance of driven piles in cohesive soils. J Geotech Geoenviron Eng 142(10):11

    Article  Google Scholar 

  9. Aksoy HS, Gor M, Inal E (2016) A new design chart for estimating friction angle between soil and pile materials. Geomech Eng 10(3):315–324

    Article  Google Scholar 

  10. Xia TA, Wang W, Wang XN (2010) Artificial neural network model for time-dependent vertical bearing capacity of preformed concrete pile. In: Tan HH (ed) Applied mechanics and mechanical engineering, pts 1–3. Trans Tech Publications Ltd, Stafa-Zurich, pp 226–230

    Google Scholar 

  11. Park HI, Cho CW (2010) Neural network model for predicting the resistance of driven piles. Mar Georesour Geotechnol 28(4):324–344

    Article  Google Scholar 

  12. Liang BL, Gao Y, Zhu L, Lv GB, Li X (2005) Prediction of pile displacement using PSO algorithm and artificial neural networks. China Univ Geosciences Press, Wuhan

    Google Scholar 

  13. Ismail A, Jeng DS (2011) Modelling load-settlement behaviour of piles using high-order neural network (HON-PILE model). Eng Appl Artif Intell 24(5):813–821

    Article  Google Scholar 

  14. Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Comput Geotech 33(8):454–459

    Article  Google Scholar 

  15. Hanna AM, Morcous G, Helmy M (2004) Efficiency of pile groups installed in cohesionless soil using artificial neural networks. Can Geotech J 41(6):1241–1249

    Article  Google Scholar 

  16. Kiefa MA (1998) General regression neural networks for driven piles in cohesionless soils. J Geotech Geoenviron Eng 124(12):1177–1185

    Article  Google Scholar 

  17. Dung NT, Chung SG, Kim SR, Beak SH (2011) Applicability of the SPT-based methods for estimating toe bearing capacity of driven PHC piles in the thick deltaic deposits. KSCE J Civ Eng 15(6):1023–1031

    Article  Google Scholar 

  18. Kumara JJ, Kikuchi Y, Kurashina T, Hyodo T (2016) Base resistance of open-ended piles evaluated by various design methods. Int J GEOMATE 11(26):2643–2650

    Google Scholar 

  19. Goh ATC (1996) Pile driving records reanalyzed using neural networks. J Geotech Eng ASCE 122(6):492–495

    Article  Google Scholar 

  20. Che WF, Lok TMH, Tam SC, Novais-Ferreira H (2003) Axial capacity prediction for driven piles at Macao using artificial neural network. A A Balkema Publishers, Leiden

    Google Scholar 

  21. Park HI, Seok JW, Hwang DJ (2006) Hybrid neural network and genetic algorithm approach to the prediction of bearing capacity of driven piles. Taylor & Francis Ltd, London

    Book  Google Scholar 

  22. Samui P (2008) Prediction of friction capacity of driven piles in clay using the support vector machine. Can Geotech J 45(2):288–295

    Article  Google Scholar 

  23. Xia T, Wang W (2009) Study on evaluating methods for time-dependent ultimate bearing capacity of single driven pile. IEEE Computer Soc, Los Alamitos

    Book  Google Scholar 

  24. Shahin MA (2010) Intelligent computing for modeling axial capacity of pile foundations. Can Geotech J 47(2):230–243

    Article  Google Scholar 

  25. Samui P (2011) Multivariate adaptive regression spline applied to friction capacity of driven piles in clay. Geomech Eng 3(4):285–290

    Article  Google Scholar 

  26. Samui P (2012) Determination of ultimate capacity of driven piles in cohesionless soil: a multivariate adaptive regression spline approach. Int J Numer Anal Meth Geomech 36(11):1434–1439

    Article  Google Scholar 

  27. Milad F, Kamal T, Nader H, Erman O (2015) New method for predicting the ultimate bearing capacity of driven piles by using Flap number. KSCE J Civ Eng 19(3):611–620

    Article  Google Scholar 

  28. Hung LC, Nguyen TD, Lee JH, Kim SR (2016) Applicability of CPT-based methods in predicting toe bearing capacities of driven piles in sand. Acta Geotech 11(2):359–372

    Article  Google Scholar 

  29. Suman S, Das SK, Mohanty R (2016) Prediction of friction capacity of driven piles in clay using artificial intelligence techniques. Int J Geotech Eng 10(5):469–475

    Article  Google Scholar 

  30. Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Process Control 21:58–73

    Article  Google Scholar 

  31. Wang S-H, Phillips P, Dong Z-C, Zhang Y-D (2018) Intelligent facial emotion recognition based on stationary wavelet entropy and jaya algorithm. Neurocomputing 272:668–676

    Article  Google Scholar 

  32. Wang S, Phillips P, Liu A, Du S (2017) Tea category identification using computer vision and generalized eigenvalue proximal svm. Fundam Inform 151(1–4):325–339

    Article  MathSciNet  Google Scholar 

  33. Vapnik V (2013) The nature of statistical learning theory. Springer, Berlin

    MATH  Google Scholar 

  34. Khemchandani R, Karpatne A, Chandra S (2011) Generalized eigenvalue proximal support vector regressor. Expert Syst Appl 38(10):13136–13142

    Article  Google Scholar 

  35. Peng X (2010) TSVR: an efficient twin support vector machine for regression. Neural Netw 23(3):365–372

    Article  Google Scholar 

  36. Khemchandani R, Goyal K, Chandra S (2016) TWSVR: regression via twin support vector machine. Neural Netw 74:14–21

    Article  Google Scholar 

  37. Jang J-S, Sun C-T (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406

    Article  Google Scholar 

  38. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, London

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, Kermanshah University of Technology, Kermanshah, Iran

    Hossein Moayedi

  2. Department of Mechanical Engineering, Kermanshah University of Technology, Kermanshah, Iran

    Sajad Hayati

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  1. Hossein Moayedi
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  2. Sajad Hayati
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Correspondence to Hossein Moayedi or Sajad Hayati.

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The authors declare that there is no conflict of interest in presenting this manuscript.

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Moayedi, H., Hayati, S. Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Comput & Applic 31, 7429–7445 (2019). https://doi.org/10.1007/s00521-018-3555-5

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  • DOI: https://doi.org/10.1007/s00521-018-3555-5

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