Abstract
This paper presents new numerical evaluations of the vertical uplift resistance of rectangular anchors located in heterogeneous and anisotropic clays obeying the Anisotropic Undrained Shear (AUS) failure criterion. The computation is based on the assumption that the anisotropic strengths of clays increase linearly with depth. To determine the uplift resistance of rectangular anchors, the uplift capacity factor Fc serves as the standardized result parameter. This factor can be determined using the finite element limit analysis (FELA) technique and is set to relate to four dimensionless parameters: the ratio of embedment ratio (H/B), the shaped ratio (L/B), the increasing strength factor (ρB/suTC0), and the anisotropic ratio (re). An analysis is carried out to explore the influence of dimensionless characteristics on developing failure mechanisms of rectangular anchors. Furthermore, this study explores the abilities of a machine learning model using the algorithm of the eXtreme Gradient Boosting (XGBoost), which has exceptional accuracy in forecasting the uplift capacity factor of rectangular anchors in heterogeneous and anisotropic clays.
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Data availability
No datasets were generated or analysed during the current study.
Abbreviations
- B :
-
Width of rectangular anchor
- L :
-
Length of rectangular anchor
- H :
-
Depth of rectangular anchor
- z :
-
Distance noted from the surface of the ground
- Q :
-
Vertical force
- q :
-
Uplift/vertical pressure
- s uTC :
-
Triaxial compressive undrained shear strength
- r e and r s :
-
Anisotropic strength factors/ The anisotropic ratio
- ρ :
-
Strength gradient
- F c :
-
Uplift capacity coefficient
- H/B :
-
The embedment ratio
- L/B :
-
The shape factor or shaped ratio
- ρ B/s uTC0 :
-
The ratio of the increasing strength
- UB:
-
Upper bound
- LB:
-
Lower bound
- Ω(Θk):
-
Regularization term
- ŷk :
-
XGBoost model's prediction
- v :
-
Learning rate
- Obj ( k ) :
-
Loss function
- λ :
-
L2 regularization term
- R2 :
-
Coefficient of determination
- RMSE:
-
Root means square error
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Funding
This research was funded by King Mongkut’s University of Technology North Bangkok with Contract no. KMUTNB-61-PHD-012.
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Contributions
Duy Tan Tran: Data curation, Software, Methodology, Writing original draft.
Tinnapat Onjaipurn: Data curation, Software, Writing - review & editing, Writing original draft.
Divesh Ranjan Kumar: Software, Validation, Writing - review and editing.
Weeraya Chim-Oye: Validation, Writing - review and editing, Superivion; Project administration.
Suraparb Keawsawasvong: Validation, Methodology, Writing - review and editing, Superivion; Project administration.
Pitthaya Jamsawang: Validation, Writing - review and editing, Superivion; Project administration.
All authors reviewed the manuscript.
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Communicated by: H. Babaie
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Tran, D.T., Onjaipurn, T., Kumar, D.R. et al. An eXtreme Gradient Boosting prediction of uplift capacity factors for 3D rectangular anchors in natural clays. Earth Sci Inform 17, 2027–2041 (2024). https://doi.org/10.1007/s12145-024-01269-8
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DOI: https://doi.org/10.1007/s12145-024-01269-8