This study examines the potential of soft computing technique—namely, Gaussian process regression (GPR), to predict ultimate bearing capacity (UBC) cohesionless soils beneath shallow foundations. The inputs model are width footing (B), depth (D), geometry (L/B), unit weight sand (γ), and internal friction angle (ϕ). results present were compared with those obtained by two theoretical approaches...

in this study, a bearing capacity device is designed and fabricated to determine the load-settlement behavior of three circular footing models resting above the ground water table. capillary rise in the studied well graded sand is measured and the level of the water table is controlled at the desired heights during each bearing capacity test. moreover, shear strength parameters and soil water r...

This study presents an analytical framework to estimate the change in ultimate bearing capacity of energy piles unsaturated fine-grained soils under drained mechanical loading conditions after heating. The was developed by extending conventional methods for temperature-dependent conditions, where thermally induced changes characteristics soil and soil–pile interface are considered. Specifically...

in shallow foundations, the third bearing capacity factor, n, has been found to show a decreasing tendency with increasing the foundation size. it is supported by experimental observations and related mainly to stress level dependent nature of the soil. on the other hand, the bearing capacity is often obtained theoretically without consideration of the foundation vertical displacements. in thi...