Behaviour of Granular Soils Under Environmentally Induced Cyclic Loads

Wichtmann, Torsten; Triantafyllidis, Theodoros

doi:10.1007/978-3-7091-1068-3_1

Cited by 4 publications

(1 citation statement)

References 151 publications

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“…It should be pointed out that the samples extracted from the quarry wall have been disturbed; Suchomel & Mašín (2011) have thus focused on quantification of variability due to soil composition reflected by the hypoplastic model parameters, not on possible variability of in situ void ratio. The testing programme consisted of the following experiments (a) angle of repose test (b) oedometric test: the oedometric specimens were prepared by air pluviation of the dry sand without any compaction, which were flooded subsequently -the aim was to prepare specimens of minimum relative density which are most suitable for asymptotic state quantification (Mašín, 2012) (c) triaxial test: the triaxial specimens were prepared by spooning saturated samples directly into the triaxial mould of diameter 38 mm, using the procedure described in Tatsuoka et al (1979) and Wichtmann & Triantafyllidis (2012). As noted by Head (1985), saturated spooning is preferable to placing the sample dry and subsequent flooding, as it is difficult to ensure full saturation in the latter case.…”

Section: Experimental Data Quantifying Total Variabilitymentioning

confidence: 99%

The influence of experimental and sampling uncertainties on the probability of unsatisfactory performance in geotechnical applications

Maš́ın

2015

Géotechnique

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While the effect of spatial variability on the probability of unsatisfactory performance in geotechnical applications is relatively well understood, comparatively less attention has been given in the literature to the effects of experimental (measurement scatter) and sampling (insufficient number of samples) uncertainties. In this paper, a general approach is developed to incorporate experimental and sampling uncertainties into probabilistic analyses based on random field methods. It is shown that, when compared with the standard approach which attributes the measured total soil variability to spatial variability, consideration of experimental uncertainty may significantly reduce the calculated probability of unsatisfactory performance. It is argued that this may be one of the reasons for an overestimation of the probability of unsatisfactory performance in geotechnical probabilistic simulations (another important reason is the spatial averaging of soil properties). Evaluation of the sampling uncertainty reveals that, although a relatively large number of samples is needed for spatial variability characterisation, a limited number of samples is sufficient to quantify the experimental uncertainty. It is pointed out that no adjustments of the existing random field-based software are needed to consider the additional uncertainties. To illustrate the proposed approach, two extensive experimental data sets on sand are presented: one reflecting total variability and the other quantifying experimental uncertainty. A hypoplastic model is calibrated against the two data sets and adopted in random field analyses of strip footing settlement.

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Section: Experimental Data Quantifying Total Variabilitymentioning

confidence: 99%

The influence of experimental and sampling uncertainties on the probability of unsatisfactory performance in geotechnical applications

Maš́ın

2015

Géotechnique

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MS‐IS Hypoplastic Model Considering Stiffness Degradation Under Cyclic Loading Conditions

Wani,

Alipour,

Kandasami

et al. 2024

Num Anal Meth Geomechanics

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Modelling the cyclic response of granular materials is key in the design of several geostructures. Over the years, numerous constitutive models have been proposed to predict the cyclic behaviour of granular materials. However, pertaining to the hypoplastic constitutive models, one of the significant limitations is their inability to accurately predict the geomechanical response during the unloading and reloading phases. This study introduces an extension of the MS‐IS hypoplastic model designed to enhance the predictions during non‐monotonic loading conditions. Addressing the limitations observed in the hypoplastic models during the unloading and reloading phases, the proposed model incorporates an additional stiffness feature. This new stiffness function is integrated into the foundational framework to enhance the model's overall stiffness response. For the unloading phase, the introduction of a stiffness degradation factor aims to modify the volumetric response and account for the realistic stiffness degradation. Additionally, for the reloading phase, stiffness is now a function of the mean effective stress. The novel model's performance is validated against experimental data, encompassing diverse loading and boundary conditions.

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