Abstract
This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (qu). This study shows that the developed GPR is a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.
Highlights
The results were compared with some popular classical methods suggested in the literature (i.e., Vesic [4]; Hansen [3]) for determining the ultimate bearing capacity (UBC) of shallow foundation on cohesionless soil
The equation proposed by Vesic [50] shows the best performance in comparison to the Hansen [3] theoretical formulas
The outputs of the used theoretical formulas are more scattered than the Gaussian process regression (GPR)-based model, as shown in this Figure 2
Summary
Ultimate bearing capacity (UBC) and allowable settlement are two important criteria to consider when designing shallow foundations. The UBC is governed by the shear strength of the soil and is estimated by theories proposed by Terzaghi [1], Meyerhof [2], Hansen [3], Vesic [4], etc. The various bearing capacity equations reveal a wide range of variations. The proposed bearing capacity theories include a number of assumptions that contribute to simplifying the problems [5]. Numerous studies proposed numerical approaches for estimating bearing capacity in addition to semi-empirical solutions for determining the bearing capacity of foundations, 4.0/)
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