Bearing capacity of strip footings on c–φ soils with square voids

Zhou, Haizuo; Zheng, Gang; He, Xinping; Xu, Xiaomin; Zhang, Tianqi; Yang, Xinyu

doi:10.1007/s11440-018-0630-0

Cited by 39 publications

(23 citation statements)

References 24 publications

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“…The results again show a good agreement with those published, and correctly bound almost all of them. Third, in Table 3, a further comparison is provided for the bearing capacity factor , allowing for the presence of a void at different depths beneath the strip footing central line, see [10,12,19]. It has been assumed that the opening width is equal to the surface footing width, and is located within an utterly cohesive soil at an undrained state with cohesion varying in the range 60 300 kPa.…”

Section: Size Of Void √ √ √mentioning

confidence: 99%

“…Xiao, Zhao, and Zhao [18] applied the same method to assess the undrained bearing capacity above voids in two-layered clays. Zhou et al [19] instead used the discontinuity layout optimization approach to determine the bearing capacity above square voids in cohesive-frictional soils, reporting typical failure patterns. Lee and Kim [20] investigated the bearing capacity above multiple square voids in sandy soils and purely cohesive clays, and they also explored the effect of load inclination.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

et al. 2021

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The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adaptive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (); root mean square error (); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring noteworthy results (0.9955 and 0.0158).

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Section: Size Of Void √ √ √mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

et al. 2021

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“…Their results indicate that the mode of failure depends on the size and location of the voids. Authors in [10] applied the procedure DLO to analyze the stability of footing situated on cohesive-frictional soil with single and dual square voids. The results implied that the presence of voids has a greater impact on c-φ soil compared to that on non-drained soil.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Effect of Underground Void on Strip Footing Sitting on a Reinforced Sand Slope with Numerical Modeling

Mazouz

Mansouri

Baazouzi

et al. 2022

Eng. Technol. Appl. Sci. Res.

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This paper presents the results of the numerical analysis undertaken to investigate the effect of the underground void on the load-bearing capacity of a strip footing placed on an unreinforced and geogrid-reinforced sand slope with a void inside. The failure mechanism of the soil was also investigated. The numerical model was obtained using 2D plane-strain FEM analysis (in Plaxis software), in which the nonlinear Mohr-Coulomb model was utilized. The effects of various parameters such as the number of geogrid layers (N), the vertical distance ratio between the top of the cavity from the base of footing (H/B), the horizontal distance of void centerline to the footing center (X/B), on the behavior of footing are studied in this research. The results indicate that there is a critical zone under the footing in which the existence of void has no influence on the bearing capacity and stability of the footing. In addition, the use of geogrid reinforcement reduces the settlement and enhances bearing capacity. Finally, the bearing capacity factor and failure mechanism increase with increasing horizontal and vertical void distances ratios (X/B and H/B) and reinforcement layers.

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“…The underground voids typically occur as a result of the dissolution of soluble rocks, the dynamic loading caused by construction and mining activities, the existence of nearby leaking pipelines, and the subsidence of poorly compacted trench backfill [2]. There are studies on the stability of footings located above voids [3][4][5][6][7][8][9][10] and footings on geosynthetic-reinforced soil with voids [11][12][13][14][15][16][17]. In the literature, researchers investigated the effect of the shape, size, location, and number of voids, as well as the type, dimension, number, and configuration of the geosynthetics.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the limit analysis theory was employed to study the bearing capacity of strip footings above voids in two-layered clays and rock mass by Xiao et al [39,40]. Zhou et al [10] extended the limit analysis to evaluate the collapse load of the footing-above-void system by using the discontinuity layout optimization (DLO) technique. However, no thorough analysis of the drained bearing capacity of strip footings on sand has been performed and the corresponding N γ values are not available in literature.…”

Section: Introductionmentioning

confidence: 99%

Stability Charts for Sustainable Infrastructure: Collapse Loads of Footings on Sandy Soil with Voids

Lee

Kim

2019

Sustainability

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The presence of underground voids in regions suitable for sustainable development can adversely affect the stability of the overlying infrastructures. In this paper, the collapse loads of strip rigid footings resting on sand with single and double continuous voids are determined for a frictional Mohr-Coulomb material following the non-associated flow rule. For use by practitioners, design charts are proposed to evaluate the well-known bearing capacity factor Nγ as a function of the dimensionless parameters related to the vertical and horizontal void distances from the footing, void shape, and spacing between the two voids, as well as the soil friction angle. The computational result compares quite favorably with the available theoretical and numerical solutions. The failure mechanism is broadly discussed based on the pattern of soil displacement around the footing and void.

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Bearing capacity of strip footings on c–φ soils with square voids

Cited by 39 publications

References 24 publications

Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

Assessing the Effect of Underground Void on Strip Footing Sitting on a Reinforced Sand Slope with Numerical Modeling

Stability Charts for Sustainable Infrastructure: Collapse Loads of Footings on Sandy Soil with Voids

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