Skip to main content
SpringerLink
Log in

Probabilistic Analysis of Pile Foundation in Cohesive Soil

  • ORIGINAL CONTRIBUTION
  • Published:
Journal of The Institution of Engineers (India): Series A Aims and scope Submit manuscript

Abstract

Pile foundation is an essential part of a structure which fulfills the load bearing requirements in the field of geotechnical engineering. Previous studies that used deterministic approaches in calculating the allowable load carrying capacity have revealed significant uncertainties. To address these uncertainties and improve accuracy, this study utilized machine learning (ML) models to suggest high-performance models for determining the allowable load carrying capacity of pile foundation. Three ML models, namely decision tree (DT), random forest, and adaptive boosting (AdaBoost), were proposed and evaluated using two datasets (70% training and 30% testing) with input variable parameters including diameter (d), cohesion (c), adhesion factor (α), and factor of safety. To authenticate the accuracy of the ML models, different statistical performance parameters were evaluated for both training and testing datasets. These parameters include trend measuring parameters (R2, VAF, LMI, a-20 index and KGE) and error measuring parameters (RMSE, NMBE, MAE, MBE, and MAD). Results showed that the DT model had the best outcomes among the three ML models. Additionally, the First-Order Second Moment was used to compute the reliability index (β) and probability of failure (Pf). Regression curves were plotted to gauge the prediction capability of different ML models; the uncertainty analysis was also performed to estimate the reliability of the ML models, while Williams plots were generated to identify the suitable region of the model. Sensitivity analysis is performed to check the impact of different input parameters on the output parameter. Overall, the study demonstrated that ML models can be used to improve the accuracy of determining the allowable load carrying capacity of pile foundation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of Data and Materials

Data presented in the paper are available with authors.

References

  1. P. Akbari, H. Patel, A review on analysis and design of pile foundation concealing different soil layers. Int. J. Eng. Dev. Res. 8, 513–516 (2020)

    Google Scholar 

  2. M. Al-Gburi, S.A. Yusuf, Investigation of the effect of mineral additives on concrete strength using ANN. Asian J. Civ. Eng. 23, 405–414 (2022). https://doi.org/10.1007/s42107-022-00431-1

    Article  Google Scholar 

  3. B.A. Bradley, M. Cubrinovski, Probabilistic pseudo-static analysis of pile foundation in liquefiable soil, in US Research Repository (2009). https://ir.canterbury.ac.nz

  4. B.A. Bradley, M. Cubrinovski, R.P. Dhakal, Performance-based seismic response of pile foundation, in ASCE 4th Geotechnical Earthquake Engineering and Soil Dynamics (2008). https://doi.org/10.1061/40975(318)84

  5. A. Borowiec, Use of probabilistic analysis in design of shallow and deep foundation. Mater. Sci. Eng. 471, 042027 (2019)

    Google Scholar 

  6. Q. Fu, X. Li, Z. Meng, Y. Liu, X. Cai, H. Fu, Reliability assessment on pile foundation bearing capacity based on the first four moments in high-order moment method. Hindawi Shock Vib. (2021). https://doi.org/10.1155/2021/2082021

    Article  Google Scholar 

  7. S. Haldar, G.L.S. Babu, Reliability measure for pile foundation based on cone penetration test data. Can. Geotech. J. 45(12), 1699–1714 (2008)

    Article  Google Scholar 

  8. A. Hussain, Probabilistic study for single pile in cohesionless soil using Monte Carlo simulation technique. Int. J. Sci. Eng. Res. 7(2), 2229–5518 (2016)

    Google Scholar 

  9. N. Kardani, A. Zhou, M. Nazem, S.L. Shen, Estimation of bearing capacity of piles in cohesionless soil using optimised machine learning approaches. Geotechn. Geol. Eng. (2019). https://doi.org/10.1007/s10706-019-01085-8

    Article  Google Scholar 

  10. B.T. Kim, G.L. Yoon, Laboratory modelling of laterally loaded pile groups in sand. KSCE J. Civ. Eng. 15(1), 65–75 (2011). https://doi.org/10.1007/s12205-011-0924-3

    Article  CAS  Google Scholar 

  11. F. Kirzhner, G. Rosenhouse, Analysis of the ultimate bearing capacity of a single pile in granular soils. Trans. Eng. Sci. 32, 207–214 (2001)

  12. D. Kolo, J.I. Aguwa, T.Y. Tsado, M. Abdullahi, Y. Abdulazeez, S. Oritola, Reliability studies on reinforced concrete beam subjected to bending forces with natural stone as course aggregate. Asian J. Civ. Eng. 22, 485–491 (2021). https://doi.org/10.1007/s42107-020-00327-y

    Article  Google Scholar 

  13. N. Kuhl, M. Goutier, R. Hirt, G. Satzger, Machine learning in artificial intelligence: towards a common understanding, in Hawaii International Conference on System Sciences (2019). https://hdl.handle.net/10125/59960

  14. P. Kumar, P. Samui, Design of an energy pile based on CPT data using soft computing techniques. Infrastructures 7, 169 (2022)

    Article  Google Scholar 

  15. M. Kumar, P. Samui, Reliability analysis of pile foundation using GMDH, GP and MARS. CIGOS Emerg. Technolog. Appl. Green Infrastruct. (2021). https://doi.org/10.1007/978-981-16-7160-9_117

    Article  Google Scholar 

  16. K. Kwak, K.J. Kim, J. Huh, J.H. Lee, J.H. Park, Reliability based calibration of resistance factors for static bearing capacity of driven steel pile. Can. Geotech. J. 47(5), 528–538 (2010). https://doi.org/10.1139/T09-119

    Article  Google Scholar 

  17. A. Mandolini, G. Russo, C. Viggiani, Pile foundation: experimental investigations, analysis and design. Ground Eng. 38(9), 34–35 (2005)

    Google Scholar 

  18. A.I. Al-Mhaidib, Loading rate effect on piles in clay from laboratory model tests. J. King Saud Univ. Eng. Sci. 13(1), 39–54 (2001). https://doi.org/10.1016/S1018-3639(18)30724-4

    Article  Google Scholar 

  19. R. Mustafa, P. Samui, S. Kumari, Reliability analysis of gravity retaining wall using hybrid ANFIS. Infrastructures 7, 121 (2022)

    Article  Google Scholar 

  20. J.H. Park, J. Huh, K.J. Kim, J. Lee, K. Kwak, Reliability analysis of static bearing capacity evaluation of driven steel piles using MCS, in Advanced Non-destructive Evaluation II (2018)

  21. Y.A. Pronozin, M.A. Stepanov, D.V. Rachkov, D.N. Davlatov, V.M. Chikishev, Laboratory investigation on interaction of the pile foundation strengthening system with the rebuilt solid pile-slab foundation. Civ. Eng. J. (2020). https://doi.org/10.28991/cej-2020-03091468

    Article  Google Scholar 

  22. N.R. Shrestha, M. Saitoh, A.K. Saha, C.S. Goit, Frequency and intensity-dependent impendence functions of laterally loaded single piles in cohesionless soil. Jpn. Geotech. Soc. https://doi.org/10.1016/j.sandf.2020.11.004

  23. L.Y. Tang, J. Wang, G.S. Yang, Y.J. Shen, P.Y. Qui, H. Liu, Reliability analysis of bearing capacity of inclined prestressed concrete pipe pile. J. Civ. Eng. Res. 6(2), 23–31 (2016)

    Google Scholar 

  24. A. Teixeira, A.G. Correia, Reliability analysis of a pile foundation in a residual soil: Contribution of the uncertainties involved and partial factors, in The 9th International Conference on Testing and Design Methods for Deep Foundations (2011)

  25. K. Winkelmann, K. Zylisnski, J. Gorski, Probabilistic analysis of settlements under a pile foundation of a road bridge pylon. Jpn. Geotech. Soc. (2020). https://doi.org/10.1016/j.sandf.2020.11.001

    Article  Google Scholar 

  26. C.F. Zhao, C. Xu, C.M. Jiao, Reliability analysis on vertical bearing capacity of bored pile determined by CPT test, in International Conference on Computational Science, pp. 1197–1204 (2007) https://doi.org/10.1007/978-3-540-72588-6_188

  27. D. Zhu, Y. Li, L. Zheng, P. Fang, X. Xie, Laboratory model study on the pile-forming mechanisms and bearing deformation characteristics of CFA piles. Hindawi Adv. Civ. Eng. (2021). https://doi.org/10.1155/2021/4827596d

    Article  Google Scholar 

  28. W. Zhu, X. Zeng, Decision tree based adaptive reconfigurable cache scheme. Algorithms 14(6), 176 (2021). https://doi.org/10.3390/a14060176

    Article  Google Scholar 

  29. X. Wang, C. Zhou, X. Xu, Application of C 4.5 decision tree for scholarship evaluations. Procedia Comput. Sci. 151, 179–184 (2019). https://doi.org/10.1016/j.procs.2019.04.027

    Article  Google Scholar 

  30. A.K. Hamoud, A.S. Hashim, W.A. Awadh, Predicting student performance in higher education institutions using decision tree analysis. Int. J. Interact. Multimed. Artif. Intell. 5, 26 (2018). https://doi.org/10.9781/ijimai.2018.02.004

    Article  Google Scholar 

  31. S.-C. Tsai, C.-H. Chen, Y.-T. Shiao, J.-S. Ciou, T.-N. Wu, Precision education with statistical learning and deep learning: a case study in Taiwan. Int. J. Educ. Technol. High. Educ. 17, 12 (2020). https://doi.org/10.1186/s41239-020-00186-2

    Article  Google Scholar 

  32. Y. Freund, R.E. Schapire, A decision–theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  Google Scholar 

  33. D.A. Opeyemi, Probabilistic failure analysis of dynamic pile capacity using Hiley and Janbu formula. J. Eng. Appl. Sci. 2, 140–149 (2010)

    Google Scholar 

  34. G.L.S. Babu, A. Srivastava, Reliability analysis of allowable pressure on shallow foundation using response surface method. Comput. Geotech. 34(3), 187–194 (2007)

    Article  Google Scholar 

  35. D.A. Opeyemi, Probabilistic failure analysis of static pile capacity for steel in cohesive and cohesionless soils. Electron. J. Geotech. Eng. 14(1), 1–12 (2019)

    Google Scholar 

  36. M. Kumar, A. Bardhan, S. Pijush, J.W. Hu, M.R. Kaloop, Reliability analysis of pile foundation using soft computing techniques. Processes 9, 486 (2021). https://doi.org/10.3390/pr9030486

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Katihar Engineering College, Katihar, Bihar, 854109, India

    Rashid Mustafa, Shashikant Suman, Ankit Kumar, Ravi Ranjan & Prince Kumar

  2. Department of Civil Engineering, Sharda University, Greater Noida, Uttar Pradesh, 20130, India

    Sufyan Ghani

Authors
  1. Rashid Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shashikant Suman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ankit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ravi Ranjan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Prince Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sufyan Ghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rashid Mustafa.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Ethics Approval and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mustafa, R., Suman, S., Kumar, A. et al. Probabilistic Analysis of Pile Foundation in Cohesive Soil. J. Inst. Eng. India Ser. A 105, 177–193 (2024). https://doi.org/10.1007/s40030-024-00785-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40030-024-00785-6

Keywords

Search

Navigation