Skip to main content

Advertisement

Log in

An extreme learning neural network approach for seismic bearing capacity estimation of planar caissons in nonhomogeneous clays

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

This study employs a two-dimensional plane strain finite element limit analysis method to evaluate the seismic bearing capacity of a planar caisson in anisotropic and non-homogeneous clay. The anisotropic behavior of the clay is simulated using the Anisotropic Undrained Shear (AUS) failure criterion in the finite element limit analysis (FELA). A rigid caisson has a depth (L) and a width (B). A comprehensive parametric analysis is executed to evaluate the non-dimensional seismic bearing capacity factor (Nce) in terms of the adhesion factor (α), anisotropic strength ratio (re), horizontal seismic coefficient (kh), depth to diameter ratio (L/D), and shear strength gradient ratio (ρB/suc0). The relationship between these parameters to the seismic bearing capacity factor is investigated, and the influence of these parameters on the potential failure mechanisms is discussed in detail. Moreover, an equation for predicting the seismic bearing capacity factor is developed through a machine learning regression approach called the Artificial Neural Network (ANN) model, which practitioners can extensively employ in the field. These correlation functions fit well with those obtained from FELA, with a value of R2 = 99.43%.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

All the data associated with the study are present in the manuscript itself.

References

Download references

Acknowledgements

We acknowledge Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for supporting this study.

Funding

This research received no specific grant from any funding agency.

Author information

Authors and Affiliations

Authors

Contributions

Van Qui Lai: Data curation, Software, Methodology, Writing original draft, Project administration; Vinay Bhushan Chauhan: Data curation, Writing - review & editing , Project administration; Suraparb Keawsawasvong: Software, Validation, Writing - review and editing; Kongtawan Sangjinda: Validation, Writing - review and editing; Jitesh T. Chavda: Validation, Writing - review and editing; Lindung Zalbuin Mase: Validation, Writing - review and editing

Corresponding author

Correspondence to Vinay Bhushan Chauhan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Communicated by: H. Babaie

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

Lai, V.Q., Chauhan, V.B., Keawsawasvong, S. et al. An extreme learning neural network approach for seismic bearing capacity estimation of planar caissons in nonhomogeneous clays. Earth Sci Inform 17, 251–270 (2024). https://doi.org/10.1007/s12145-023-01175-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-01175-5

Keywords

Navigation