Prediction of Ultimate Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Gaussian Process Regression Approach

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.

Prediction of Pile Bearing Capacity Using XGBoost Algorithm: Modeling and Performance Evaluation

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“…R 2 is a number that ranges from 0 to 1, a higher R 2 value indicates a more efficient model. The model is considered effective when R 2 is more than 0.8 and close to 1 [22]. The mean squared difference between projected outputs and targets is the criterion RMSE, and the mean magnitude of errors is the criterion MAE, RMSE and MAE are similar in that the closer these criterion values of these errors are to 0, the better the model's performance.…”

“…The sensitivity results of the XGBoost model were assessed using Yang and Zang's [67] method for assessing the impact of input variables on Pu. This approach, which has been used in several investigations [22,28,[68][69][70], is as follows:…”

“…A recently developed approach based on data mining techniques has been increasingly employed to resolve real-world problems for the past half-decade, particularly in the field of civil engineering [18][19][20][21][22][23][24][25][26][27][28]. Several practical problems have already been effectively performed using machine learning algorithms, paving the way for new prospects in the construction industry.…”

Effective Hybrid Soft Computing Approach for Optimum Design of Shallow Foundations

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