Abstract
The purpose of this research is to use machine learning approaches to make predictions about the dimensionless bearing capacity (DBCp) of a circular footing on layered sand subjected to an inclined load. To achieve this objective, the finite element method was applied to the literature in order to collect 2400 data sets for the circular footing on layered sand under inclination loads. As independent variables, the embedment ratio (u/D), thickness ratio (H/D), load inclination angle (α1/90°), unit weight ratio of the loose sand layer to the dense sand layer (γ2/γ1) and friction angle ratio of the loose sand layer to the dense sand layer (φ2/φ1) were used to predict the dimensional bearing capacity (DBCP). The impact of each independent variable on the overall structural integrity was also analyzed through sensitivity analysis. The results demonstrate that load inclination is the primary factor impacting in the dimensionless bearing capacity. Finally, the performance of the machine learning model was assessed by means of six statistical parameters. The developed model was proven to be effective for predicting the dimensionless bearing capacity of the circular footing on layered sand under inclined loading.
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Data availability
The data that support the findings of this study are available from the corresponding author, [SPS], upon reasonable request.
Abbreviations
- φ1, φ2 :
-
Soil friction angle for upper dense sand and Lower loose sand soil, in degree
- γ1, γ2 :
-
Unit weight of the upper dense sand soil and lower loose sand soil, kN/m3
- E1, E2 :
-
Elastic moduli for upper dense sand and lower loose sand layer
- D:
-
Diameter of the footing
- α1 :
-
Concentric load inclination angle with respect to vertical acting on the circular footing, in degree
- H:
-
Thickness of the upper dense sand layer
- u:
-
Depth of the embedded footing from ground surface
- u/D:
-
Embedded depth ratio
- H/D:
-
Thickness ratio
- φ2/φ1 :
-
Soil friction ratio
- γ2/γ1 :
-
Unit weight ratio
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The authors are grateful to the supervisor (Dr. A.K. Roy), for providing his keen interest, guidance during the work of this research paper.
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Concept authorship- S.P.S. Preparation of assumptions and methods- S.P.S. and A.K.R. Performance of research- S.P.S. Analysis of results and formulation of conclusions- S.P.S.
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Singh, S.P., Roy, A.K. Machine learning techniques to predict the dimensionless bearing capacity of circular footing on layered sand under inclined loads. Multiscale and Multidiscip. Model. Exp. and Des. 6, 579–590 (2023). https://doi.org/10.1007/s41939-023-00176-7
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DOI: https://doi.org/10.1007/s41939-023-00176-7