Rock Mechanics
Jitendra Singh Yadav; Poonam Shekhawat; Sreekeshava K S
Abstract
The present work aims to assess the pressure-settlement behaviour of sand beds under a square footing reinforced with coir geotextile using the PLAXIS 3D software. The angle of internal friction of sand was varied from 28° to 38°. The effect of length of coir geotextile (1B, 2B, 3B, 4B, and 5B; ...
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The present work aims to assess the pressure-settlement behaviour of sand beds under a square footing reinforced with coir geotextile using the PLAXIS 3D software. The angle of internal friction of sand was varied from 28° to 38°. The effect of length of coir geotextile (1B, 2B, 3B, 4B, and 5B; B is width of footing) and position of coir geotextile (0.2B, 0.4B, 0.6B, 0.8B, and 1B) to ultimate bearing capacity of sand were examined. A remarkable improvement in ultimate bearing capacity of sand beds was obtained with provision of coir geotextiles. It was observed that the bearing capacity of sand increases by placing coir geotextiles up to a depth of 0.4B from base of footing, thereafter it starts decreasing. The optimum length of coir geotextile was found as 4B-5B. An insignificant improvement in the bearing capacity ratio of sand reinforced with coir geotextile was observed at higher values of angle of internal friction.
Rock Mechanics
Mounius Bashir; Manendra Singh; Krishna Kotiyal
Abstract
Among all methods for ground improvement, stone columns have become more popular recently, owing to their simple construction and plentiful availability of raw materials. However, in relatively softer soils, ordinary stone columns (OSCs) experience significant bulging owing to the minimal confinement ...
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Among all methods for ground improvement, stone columns have become more popular recently, owing to their simple construction and plentiful availability of raw materials. However, in relatively softer soils, ordinary stone columns (OSCs) experience significant bulging owing to the minimal confinement offered by the surrounding soil. This necessitates the introduction of reinforcements in the stone column, to enhance their strength in such circumstances. The subject of this investigation was the assessment of the behavior of horizontally reinforced stone columns (HRSCs), introduced in layered soil, under the raft foundation. The soil material included was idealised using an isotropic linearly elastic fully plastic model with a Mohr-Coulomb failure criterion. There are a total of six separate factors required by the Mohr-Coulomb criterion. These include cohesion (c), the soil's dry unit weight (γd), the Poisson ratio (μ), the angle of internal friction (φ), the angle of dilatancy (ψ), and the Young's modulus of elasticity (E). At the very beginning, the load-settlement response of unreinforced soil was evaluated followed by a comparative study between square and triangular arrangements of stone columns, at different spacings, under the raft, to arrive at the configuration that encounters minimal settlements and lateral deformations. Furthermore, circular discs of suitable geogrid material were introduced along the length of the stone column. The elastic behaviour of geogrids is governed by two properties: tensile modulus and yield strength. The load-settlement behavior and lateral deformations of the resulting reinforced stone columns, with OSCs were compared. Furthermore, the spacing between the circular discs of geogrids was kept at D/2, D, 2D, and 3D, where D is the diameter of the stone column. According to the findings of an investigation conducted using FEM software, the performance of a granular pile group that is laid out in the shape of a triangle encounters much less lateral deformation and settlement than the square arrangement. The results also show that the performance of HRSCs was way better than those of OSCs, under the same in-situ soil conditions.
Rock Mechanics
Anant Saini; Jitendra Singh Yadav
Abstract
The goal of this research work was to use an Artificial Neural Network (ANN) model to predict the ultimate bearing capacity of circular footing resting on recycled construction waste over loose sand. A series of plate load tests were conducted by varying the thickness of two sizes of recycled construction ...
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The goal of this research work was to use an Artificial Neural Network (ANN) model to predict the ultimate bearing capacity of circular footing resting on recycled construction waste over loose sand. A series of plate load tests were conducted by varying the thickness of two sizes of recycled construction waste (5 mm and 10.6 mm) layer (0.4d, 0.6d, 0.8d, 1d, and 1.2d, d: diameter of footing) prepared at different relative densities (30%, 50%, and 70%) overlaying. The ultimate bearing capacity obtained for various combinations was used to develop the ANN model. The input parameters of the ANN model were thickness of recycled construction waste layer to diameter of circular footing ratio, angle of internal friction of sand, unit weight of sand, angle of internal friction of recycled construction waste and unit weight of recycled construction waste, and the model's output parameter was ultimate bearing capacity. The FANN-SIGMOD_SYMMETRIC model with topology 3-2-1 provided a higher estimate of the ultimate bearing capacity of circular footing, according to the ANN findings. The sensitivity analysis also revealed that the unit weight of sand and angle of internal friction of sand had insignificant effects on ultimate bearing capacity. The estimated ultimate bearing capacity was most affected by the angle of internal friction of recycled construction waste. The result of multiple linear regression analysis was not as good as the ANN model at predicting the ultimate bearing capacity.
