The effects of induced anisotropy on the undrained behaviour of very loose and saturated sands have been a subject of intensive investigation, both experimentally and theoretically, by several authors in the past few years. This paper proposes an original constitutive model well-adapted to simulate the behaviour of sands subject to complex stress histories, in particular, the preloading cycle along the classical drained stress path in compression. The developed model belongs to the family of critical state models. Its construction is based on a few theoretical concepts taken from the theory of 'Bounding Surface Plasticity' developed among others by Y. Dafalias and Popov (1975), the 'Clay And Sand Model' (CASM) of H. Yu (2006), the CJS model (B. Cambou and K. Jafari (1988)) and the hyperelastic isotropic model of P. Lade (1987). To accurately simulate volume changes, which represent a key element in soil behaviour, a state-dependent dilatancy rule is proposed, which can account for the influences of stress and void ratio. The current void ratio depends implicitly on the irreversible strains already accumulated hence the strain history. A kinematic hardening is combined with an isotropic hardening, involving rotation and distortion of the bounding surface, in order to capture correctly the experimental observations. Comparisons of experimental results to numerical simulations show that the model is able to simulate with a good precision the major trends of undrained responses of loose and presheared sands. It predicts correctly rapid static liquefaction at small or null drained preloading, as well as the progressive transition to a completely stable behaviour typical of dense sands, while the sample is loose in reality. At intermediate to large amplitudes of preloadings, the model also predicts correctly the temporary stage of instability when the deviatoric stress decreases slightly before rising up again. Hardening modulus at current stress point. According to Bardet [49], the hardening modulus depends on the image stress point and the distance δ separating the later from the current stress point.
We present a new system for video auto tagging which aims at correcting the tags provided by users for videos uploaded on the Internet. Unlike most existing systems, in our proposal, we do not use the questionable textual information nor any supervised learning system to perform a tag propagation. We propose to compare directly the visual content of the videos described by different sets of features such as Bag-Of-visual-Words or frequent patterns built from them. We then propose an original tag correction strategy based on the frequency of the tags in the visual neighborhood of the videos. Experiments on a Youtube corpus show that our method can effectively improve the existing tags and that frequent patterns are useful to construct accurate visual features.
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