Numerical analysis of liquefaction-induced bearing capacity degradation of shallow foundations on a two-layered soil profile

AbstractThe presence of the groundwater level (GWL) at the rock mass may significantly affect the mechanical behavior, and consequently the bearing capacity. The water particularly modifies two aspects that influence the bearing capacity: the submerged unit weight and the overall geotechnical quality of the rock mass, because water circulation tends to clean and open the joints. This paper is a study of the influence groundwater level has on the ultimate bearing capacity of shallow foundations on the rock mass. The calculations were developed using the finite difference method. The numerical results included three possible locations of groundwater level: at the foundation level, at a depth equal to a quarter of the footing width from the foundation level, and inexistent location. The analysis was based on a sensitivity study with four parameters: foundation width, rock mass type (mi), uniaxial compressive strength, and geological strength index. Included in the analysis was the influence of the self-weight of the material on the bearing capacity and the critical depth where the GWL no longer affected the bearing capacity. Finally, a simple approximation of the solution estimated in this study is suggested for practical purposes.

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Numerical Analysis of Bearing Capacity of a Ring Footing on Geogrid Reinforced Sand

Buildings ◽

10.3390/buildings11020068 ◽

2021 ◽

Vol 11 (2) ◽

pp. 68

Author(s):

Haidar Hosamo ◽

Iyad Sliteen ◽

Songxiong Ding

Keyword(s):

Numerical Analysis ◽

Bearing Capacity ◽

Double Layer ◽

Practical Importance ◽

Single Layer ◽

Outer Diameter ◽

Storage Container ◽

Reinforced Sand ◽

Oil Storage ◽

Ring Footing

A ring footing is found to be of practical importance in supporting symmetrical constructions for example silos, oil storage container etc. In the present paper, numerical analysis was carried out with explicit code FLAC3D 7.0 to investigate bearing capacity of a ring footing on geogrid reinforced sand. Effects of the ratio n of its inner/outer diameter (Di/D) of a ring footing, an optimum depth to lay the geogrid layer were examined. It was found that an intersection zone was developed in soil under inner-side (aisle) of ring footing, contributing to its bearing capacity. Substantial increase of bearing capacities could be realized if ratio n of a ring footing was around 0.6. Numerical results also showed that, bearing capacity of a ring footing could increase significantly if a single-layer geogrid was laid at a proper depth under the footing. Similar contribution was found if a double-layer geogrid was implemented. However, such increases appeared to be rather limited if a triple-layer geogrid or a four-layer geogrid was used. A double-layer geogrid was recommended to increase the bearing capacity of a ring footing; the depth to lay this double-layer geogrid was also discussed.

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Bearing capacity improvement of shallow foundations using a trench filled with granular materials and reinforced with geogrids

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07679-y ◽

2021 ◽

Vol 14 (15) ◽

Author(s):

Mohammad Mahdi Hajitaheriha ◽

Davood Akbarimehr ◽

Amin Hasani Motlagh ◽

Hossein Damerchilou

Keyword(s):

Bearing Capacity ◽

Granular Materials ◽

Shallow Foundations ◽

Capacity Improvement

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Application of Artificial Neural Networks for Predicting the Bearing Capacity of Shallow Foundations on Rock Masses

Rock Mechanics and Rock Engineering ◽

10.1007/s00603-021-02549-1 ◽

2021 ◽

Author(s):

M. A. Millán ◽

R. Galindo ◽

A. Alencar

Keyword(s):

Bearing Capacity ◽

Analytical Solutions ◽

Shear Failure ◽

Numerical Models ◽

Computational Effort ◽

Shallow Foundations ◽

Geological Strength Index ◽

Rock Masses ◽

Ann Model ◽

Artificial Neural

AbstractCalculation of the bearing capacity of shallow foundations on rock masses is usually addressed either using empirical equations, analytical solutions, or numerical models. While the empirical laws are limited to the particular conditions and local geology of the data and the application of analytical solutions is complex and limited by its simplified assumptions, numerical models offer a reliable solution for the task but require more computational effort. This research presents an artificial neural network (ANN) solution to predict the bearing capacity due to general shear failure more simply and straightforwardly, obtained from FLAC numerical calculations based on the Hoek and Brown criterion, reproducing more realistic configurations than those offered by empirical or analytical solutions. The inputs included in the proposed ANN are rock type, uniaxial compressive strength, geological strength index, foundation width, dilatancy, bidimensional or axisymmetric problem, the roughness of the foundation-rock contact, and consideration or not of the self-weight of the rock mass. The predictions from the ANN model are in very good agreement with the numerical results, proving that it can be successfully employed to provide a very accurate assessment of the bearing capacity in a simpler and more accessible way than the existing methods.

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