Geomechanics and Engineering

Techno-Press (테크노프레스)
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Development of an optimized model to compute the undrained shaft friction adhesion factor of bored piles

  • Alzabeebee, Saif (Department of Roads and Transport Engineering, College of Engineering, University of Al-Qadisiyah) ;
  • Zuhaira, Ali Adel (Technical Institute of Al-Najaf, Al-Furat Al-Awsat Technical University) ;
  • Al-Hamd, Rwayda Kh. S. (School of Applied Sciences, Abertay University)
  • Received : 2021.09.06
  • Accepted : 2022.01.09
  • Published : 2022.02.25
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Abstract

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

Keywords

References

  1. Ahangar-Asr, A., Javadi, A.A., Johari, A. and Chen, Y. (2014), "Lateral load bearing capacity correlationling of piles in cohesive soils in undrained conditions: an intelligent evolutionary approach", Appl. Soft Comput., 24, 822-828. https://doi.org/10.1016/j.asoc.2014.07.027.
  2. Alani, A.M. and Faramarzi, A. (2014), "An evolutionary approach to correlationling concrete degradation due to sulphuric acid attack", Appl. Soft Comput., 24, 985-993. https://doi.org/10.1016/j.asoc.2014.08.044.
  3. Alkroosh, I., Alzabeebee, S. and Al-Taie, A.J. (2020), "Evaluation of the accuracy of commonly used empirical correlations in predicting the compression index of Iraqi fine-grained soils", Innov. Infrastruct. Solut., 5(3), 1-10. https://doi.org/10.1007/s41062-020-00321-y.
  4. Alzabeebee, S. (2020a), "Application of EPR-MOGA in computing the liquefaction-induced settlement of a building subjected to seismic shake", Eng. Comput., https://doi.org/10.1007/s00366-020-01159-9.
  5. Alzabeebee, S. (2020b), "Dynamic response and design of a skirted strip foundation subjected to vertical vibration", Geomech. Eng., 20(4), 345-358. https://doi.org/10.12989/gae.2020.20.4.345.
  6. Alzabeebee, S. and Chapman, D.N. (2020), "Evolutionary computing to determine the skin friction capacity of piles embedded in clay and evaluation of the available analytical methods", Transp. Geotechn., 100372. https://doi.org/10.1016/j.trgeo.2020.100372
  7. Alzabeebee, S., Alshkane, Y.M., Al-Taie, A.J. and Rashed, K.A. (2021a), "Soft computing of the recompression index of fine-grained soils", Soft Comput., 25(24), 15297-15312. https://doi.org/10.1007/s00500-021-06123-3.
  8. Alzabeebee, S., Mohamad, S.A. and Al-Hamd, R.K.S. (2021b), "Surrogate models to predict maximum dry unit weight, optimum moisture content and California bearing ratio form grain size distribution curve", Road Mater. Pav. Design, https://doi.org/10.1080/14680629.2021.1995471.
  9. Alzabeebee, S. (2022), "Explicit soft computing model to predict the undrained bearing capacity of footing resting on aggregate pier reinforced cohesive ground", Innov. Infrastruct. Solut., 7(1), 1-10. https://doi.org/10.1007/s41062-021-00706-7.
  10. Armaghani, D.J., Mamou, A., Maraveas, C., Roussis, P.C., Siorikis, V.G., Skentou, A.D. and Asteris, P.G. (2021), "Predicting the unconfined compressive strength of granite using only two non-destructive test indexes", Geomech. Eng, 25(4), 317-330. https://doi.org/10.12989/gae.2021.25.4.317.
  11. Bai, X.D., Cheng, W.C., Ong, D.E. and Li, G. (2021), "Evaluation of geological conditions and clogging of tunneling using machine learning", Geomech. Eng., 25(1), 59-73. https://doi.org/10.12989/gae.2021.25.1.059.
  12. Chen, Y.J. and Kulhawy, F.H. (1994), "Case history evaluation of the behavior of drilled shafts under axial and lateral loading", Rep. No. TR-104601, Electric Power Research Institute, Palo Alto, Calif.
  13. Cherubini, C. and Yves, R. (1998), "A few comments on pile design: Discussion/Reply", Can. Geotech. J., 35(5), 905. https://doi.org/10.1139/t98-036.
  14. Coduto, D.P. (1994), Foundation design, principles and practices. Prentice Hall Inc., Englewood Cliffs, N.Y.
  15. Dias, D. and Grippon, J. (2017), "Numerical modelling of a pile-supported embankment using variable inertia piles", Struct, Eng, Mech., 61(2), 245-253. https://doi.org/10.12989/sem.2017.61.2.245.
  16. Giustolisi, O. and Savic, D.A. (2006), "A symbolic data-driven technique based on evolutionary polynomial regression", J. Hydroinform., 8(3), 207-222. https://doi.org/10.2166/hydro.2006.020b.
  17. Giustolisi, O. and Savic, D.A. (2009), "Advances in data-driven analyses and correlationling using EPR-MOGA", J. Hydroinform., 11(3-4), 225-236. https://doi.org/10.2166/hydro.2009.017.
  18. Goh, A.T., Kulhawy, F.H. and Chua C.G. (2005), "Bayesian neural network analysis of undrained side resistance of drilled shafts", J. Geotech. Geoenv. Eng., 131(1), 84-93. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:1(84).
  19. Goh, A.T.C., Zhang, R.H., Wang, W., Wang, L., Liu, H.L. and Zhang, W.G. (2020), "Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils", Acta Geotech., 15(5), 1259-1272. https://doi.org/10.1007/s11440-019-00843-5.
  20. Hwang, T.H., Kim, K.H. and Shin, J.H. (2017), "Bearing capacity of micropiled-raft system", Struc, Eng, Mech., 63(3), 417-428. https://doi.org/10.12989/sem.2017.63.3.417.
  21. Kulhawy, F.H. and Jackson, C. (1989), "Some observations of undrained side resistance of drilled shafts", Foundation Engineering: Current principles and practices, 2, 1011-1025.
  22. Liu, L., Moayedi, H., Rashid, A.S.A., Rahman, S.S.A. and Nguyen, H. (2020), "Optimizing an ANN correlation with genetic algorithm (GA) predicting load-settlement behaviours of eco-friendly raft-pile foundation (ERP) system", Eng. Comput., 36(1), 421-433. https://doi.org/10.1007/s00366-019-00767-4.
  23. Luat, N.V., Lee, K. and Thai, D.K. (2020a), "Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils", Geomech. Eng., 20(5), 385-397. https://doi.org/10.12989/gae.2020.20.5.385.
  24. Luat, N.V., Nguyen, V.Q., Lee, S., Woo, S. and Lee, K. (2020b), "An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils", Geomech. Eng., 21(6), 583-598. https://doi.org/10.12989/gae.2020.21.6.583.
  25. Moayedi, H., Moatamediyan, A., Nguyen, H., Bui, X.N., Bui, D.T. and Rashid, A.S.A. (2020), "Prediction of ultimate bearing capacity through various novel evolutionary and neural network correlations", Eng. Comput., 36, 671-687. https://doi.org/10.1007/s00366-019-00723-2.
  26. Nguyen, H., Moayedi, H., Foong, L.K., Al Najjar, H.A.H., Jusoh, W.A.W., Rashid, A.S.A. and Jamali, J. (2020b), "Optimizing ANN correlations with PSO for predicting short building seismic response", Eng. Comput., 36, 823-837. https://doi.org/10.1007/s00366-019-00733-0.
  27. Nguyen, H., Moayedi, H., Jusoh, W.A.W. and Sharifi, A. (2020a), "Proposing a novel predictive technique using M5Rules-PSO correlation estimating cooling load in energy-efficient building system", Eng. Comput., 36, 857-7866. https://doi.org/10.1007/s00366-019-00735-y.
  28. Robert, Y. (1997), "A few comments on pile design", Can. Geotech. J., 34(4), 560-567. https://doi.org/10.1139/t97-024.
  29. Sladen, J.A. (1992), "The adhesion factor: applications and limitations", Can. Geotech. J., 29(2), 322-326. https://doi.org/10.1139/t92-036.
  30. Tang, L. and Na, S. (2021), "Comparison of machine learning methods for ground settlement prediction with different tunneling datasets", J. Rock Mech. Geotech. Eng., https://doi.org/10.1016/j.jrmge.2021.08.006.
  31. Wang, H., Moayedi, H. and Foong, L.K. (2020), "Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design", Eng. Comput., https://doi.org/10.1007/s00366-020-00957-5.
  32. Weltman, A.J. and Healy, P.R. (1978), "Piling in 'Boulder Clay' and Other Glacial Tills", Construction Industry Research and Information Association, Report PG5.
  33. Zhang, R.H., Li, Y.Q., Goh, A.T.C. and Zhang, W.G. (2021c), "Analysis of ground surface settlement in anisotropic clays using XGBoost and RFR models", J. Rock Mech. Geotech. Eng., https://doi.org/10.1016/j.jrmge.2021.08.001.
  34. Zhang, W. and Goh, A.T. (2016), "Multivariate adaptive regression splines and neural network models for prediction of pile drivability", Geosci. Front, 7(1), 45-52. https://doi.org/10.1016/j.gsf.2014.10.003.
  35. Zhang, W., Li, H., Li, Y., Liu, H., Chen, Y. and Ding, X. (2021b), "Application of deep learning algorithms in geotechnical engineering: a short critical review", Artif. Intell. Rev., 54, 5633-5673. https://doi.org/10.1007/s10462-021-09967-1.
  36. Zhang, W., Wu, C., Zhong, H., Li, Y. and Wang, L. (2021a), "Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization", Geosci. Front., 12(1), pp. 469-477. https://doi.org/10.1016/j.gsf.2020.03.007
  37. Zhang, W., Zhang, R., Wu, C., Goh, A.T.C., Lacasse, S., Liu, Z. and Liu, H. (2020), "State-of-the-art review of soft computing applications in underground excavations", Geosci. Front., 11(4), 1095-1106. https://doi.org/10.1016/j.gsf.2019.12.003.
  38. Zhang, W.G. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95. https://doi.org/10.1016/j.compgeo.2012.09.016.