2017
DOI: 10.1051/epjconf/201714009038
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Erosion of cohesive soil layers above underground conduits

Abstract: Using a recently developed 2D numerical modelling that combines Discrete Element (DEM) and Lattice Boltzmann methods (LBM), we simulate the destabilisation by an hydraulic gradient of a cohesive granular soil clogging the top of an underground conduit. We aim to perform a multi-scale study that relates the grain scale behavior to the macroscopic erosion process. In particular, we study the influence of the flow conditions and the inter-particle contact forces intensity on the erosion kinetic.

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Cited by 3 publications
(2 citation statements)
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“…As part of the nation-wide studies on dyke stability, the IRSTEA is used to perform numerical modeling and laboratory experimentation of the horizontal internal erosion, resulting in weakening or failure of these sensitive earthworks. In 2016, the BRGM and the IRSTEA began a scientific partnership to adjust the horizontal modeling in order to address the vertical internal erosion in the covered karst terrains (Luu et al, 2017).…”
Section: Numerical Modeling Of the Internal Processmentioning
confidence: 99%
“…As part of the nation-wide studies on dyke stability, the IRSTEA is used to perform numerical modeling and laboratory experimentation of the horizontal internal erosion, resulting in weakening or failure of these sensitive earthworks. In 2016, the BRGM and the IRSTEA began a scientific partnership to adjust the horizontal modeling in order to address the vertical internal erosion in the covered karst terrains (Luu et al, 2017).…”
Section: Numerical Modeling Of the Internal Processmentioning
confidence: 99%
“…Drilling is one of the important and only source to gain water from underground. The water has different underground levels, and the different layers of the land layers lead us to the water [4]. In this study, the soil color and land layer are predicted by using the proposed DE-based weighted voting classifier, and results are compared with some state-of-theart ML algorithms and ensemble voting classifier [5].…”
Section: Introductionmentioning
confidence: 99%