Effect of relative density and stress level on the bearing capacity of footings on sand

Loukidis, Dimitrios; Salgado, Rodrigo

doi:10.1680/geot.8.p.150.3771

Cited by 67 publications

(26 citation statements)

References 36 publications

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“…The shear strength parameters of sand A vary in function of the normal stress level as shown in Figure 3 These results are in good agreement with data reported in the literature (Celauro et al, 2014;Chakraborty and Salgado, 2010;Lancelot et al, 2006;Loukidis and Salgado, 2011;Negussey and Vaid, 1990;Ponce and Bell, 1971;Rowe, 1962;Sture et al, 1998;Yamaguchi et al, 1977).…”

Section: Sandssupporting

confidence: 89%

The bearing capacity of footings on sand with a weak layer

Valore

Ziccarelli

Muscolino

2017

Geotechnical Research

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Minor details of the ground, such as thin weak layers, shear bands and slickensided surfaces, can substantially affect the behaviour of soil-footing and other geotechnical systems, despite their seeming insignificance. In this paper, the influence of the presence of a thin horizontal weak layer on the ultimate bearing capacity of a strip footing on dense sand is investigated by single-gravity tests on small-scale physical models of the soil-footing system. The test results show that the weak layer strongly influences both the failure mechanism and the ultimate bearing capacity if its depth is lower than about four times the footing width. It is found that the presence of a thin weak layer can cause decreases of the ultimate bearing capacity of up to 80%. Numerical simulations, by finite-element analysis, of the behaviour of the reduced-scale models are able to capture the failure mechanism and the ultimate bearing capacity correctly, only if the mean equivalent constant value of the secant angle of shearing resistance used in calculations is selected, taking into account the curvature of the shear strength envelope of the sand within the very low normal stress range existing in the tested models.

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Section: Sandssupporting

confidence: 89%

The bearing capacity of footings on sand with a weak layer

Valore

Ziccarelli

Muscolino

2017

Geotechnical Research

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“…(14) to calculate Nhp0. Note that a similar approach of using  e ′ to calculate the 455 bearing capacity of footing on dense sand, where shear bands form progressively, has been 456 presented by Loukidis and Salgado (2011). Similarly, a representative value of  (< p ′ ) has also 457 been used to calculate the anchor resistance (Dickin and Leung 1983;Dickin 1994).…”

Section: Proposed Simplified Equations 427mentioning

confidence: 99%

Lateral resistance of pipes and strip anchors buried in dense sand

et al. 2018

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34The response of buried pipes and vertical strip anchors in dense sand under lateral loading is 35 compared based on finite-element (FE) modeling. Incorporating strain-softening behaviour of 36 dense sand, the progressive development of shear bands and the mobilization of friction and 37 dilation angles along the shear bands are examined, which can explain the variation of peak and 38 post-peak resistances for anchors and pipes. The normalized peak resistance increases with 39 embedment ratio and remains almost constant at large burial depths. When the height of an anchor 40 is equal to the diameter of the pipe, the anchor gives approximately 10% higher peak resistance 41 than that of the pipe. The transition from the shallow to deep failure mechanisms occurs at a larger 42 embedment ratio for anchors than pipes. A simplified method is proposed to estimate the lateral 43 resistance at the peak and also after softening at large displacements. 44Page 3 of 33 (LE) method overpredicts the maximum uplift resistance (mean value) of pipes by 11%, while it 57 underpredicts the anchor resistance by 14%. The authors suggested that this discrepancy might 58 result simply from the feature of the database or be an indication that pipes and anchors behave 59 differently. 60Very limited research comparing lateral resistance of pipes and anchors is available. In a limited 61 number of centrifuge tests, Dickin (1988) showed no significant difference between the force-62 displacement curves for pipes and anchors up to the peak resistance; however, the anchors give 63 higher resistance than pipes after the peak. 64Pipelines and anchors buried in dense sand are the focus of the present study. Anchors can be 65 installed directly in dense sand (Das and Shukla 2013). Buried pipelines are generally installed 66 into a trench. When the trench is backfilled with sand, the backfill material might be in a loose to 67 medium dense state. However, during the lifetime of an onshore pipeline, the backfill sand might 68 be densified due to traffic loads, nearby machine vibrations or seismic wave propagation 69 (Kouretzis et al. 2013). Furthermore, Clukey et al. (2005 showed that the relative density of sandy 70 backfill of an offshore pipe section increased from less than ~ 57% to ~ 85-90% in 5 months after 71 construction, which has been attributed to wave action at the test site in the Gulf of Mexico. The 72 behaviour of buried pipes and anchors can be compared through physical modeling and numerical 73 analysis. Physical modeling is generally expensive, especially the full-scale tests at large burial 74 depths, in addition to having some inherent difficulties, including the examination of the 75 progressive formation of thin shear bands in dense sand. Through a joint research project between 76

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“…To conduct an inverse analysis, a function that can evaluate the error between the experimental and numerical results must be defined and then be minimized. For each test involved in the optimization, the difference between the experimental result and the numerical prediction is measured by a norm value, referred to as an individual norm that forms an error function Error(x), Error x ð Þ→ min; (1) where x is a vector containing the parameters to be optimized. Bound constraints are introduced on these variables,…”

Section: Formulation Of An Error Function (Fitness Function)mentioning

confidence: 99%

“…Finite element analyses and analytical solution‐based tools are widely used in geotechnical engineering, such as predicting the bearing capacity of foundations (Loukidis and Salgado), calculating the safety factor of slopes (Griffiths and Lane), predicting the ground settlement of embankments (eg, Shen et al; Wu et al; Karstunen and Yin) or tunnel (Shen et al), and predicting the deformation of retaining wall during excavation (Ou et al). For all these cases, a common requirement is to adequately obtain the soil properties and the design parameters from laboratory or field tests measurements (Yao et al).…”

Section: Introductionmentioning

confidence: 99%

Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

Yin

Jin

Shen

et al. 2017

Num Anal Meth Geomechanics

235

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A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder-Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr-Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay.

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Effect of relative density and stress level on the bearing capacity of footings on sand

Cited by 67 publications

References 36 publications

The bearing capacity of footings on sand with a weak layer

The bearing capacity of footings on sand with a weak layer

Lateral resistance of pipes and strip anchors buried in dense sand

Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

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