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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...

Determining the ultimate bearing capacity (UBC) is vital for design of shallow foundations. Recently, soft computing methods (i.e. artificial neural networks and support vector machines) have been used for this purpose. In this paper, Random Forest (RF) is utilized as a tree-based ensemble classifier for predicting the UBC of shallow foundations on cohesionless soils. The inputs of model are wi...

Random field modelling of soil variability allows significant statistical results to be inferred from field data; moreover, it provides a consistent framework for incorporating such variability in reliability-based design. Cone penetration testing (CPT) is increasingly appreciated because of its near-continuity and repeatability. Stressnormalised CPT parameters are included in widely used engin...

bearing capacity prediction of shallow foundation is one of the most important problems in geotechnical engineering practices, with a wide variety range of methods which have been introduced to forecast it accurately. recently, soft computing methods such as artificial neural networks (anns) and support vector machines (svms) have been used for prediction of the ultimate bearing capacity of sha...

Loosely packed cohesionless soils may suffer partial or complete liquefaction during seismic loading, causing significant structural damage. The dynamic behavior of liquefiable is widely investigated through element testing under controlled cyclic loading in undrained conditions. In this work, a total 20 stress-controlled triaxial experiments were conducted on saturated specimens clean sand to ...

The study aims to construct a framework for CPT based ultimate pile capacity calculation cohesionless soils with random field theory. Cone tip resistance (qc) was taken as the spatially varying parameter constant mean and changing coefficients of variation. profiles were simulated generations, single (Qu) calculated these simulations. influence spatial variation qc on Qu investigated. proposed ...