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

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 (<i>B</i>), depth of footing (<i&...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Mahmood Ahmad, Feezan Ahmad, Piotr Wróblewski, Ramez A. Al-Mansob, Piotr Olczak, Paweł Kamiński, Muhammad Safdar, Partab Rai
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
T
Acceso en línea:https://doaj.org/article/9500e13ff9ec412f982811748e6838e8
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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 (<i>B</i>), depth of footing (<i>D</i>), footing geometry (<i>L</i>/<i>B</i>), unit weight of sand (<i>γ</i>), and internal friction angle (<i>ϕ</i>). 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 (<i>q<sub>u</sub></i>). This study shows that the developed GPR is a robust model for the <i>q<sub>u</sub></i> prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.