Investigating the Formation of a Filter Cake in Column Experiments, for Combinations of Filter and Fine Sand in a Coarse ... View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Sepideh Akrami , Adam Bezuijen , Esther Rosenbrand , André Koelewijn , Ulrich Förster , Vera van Beek

ABSTRACT

The coarse sand barrier (CSB) is a single granular filter used to retrofit an existing structure, making it more stable against backward erosion piping. The barrier material should be chosen carefully to retain particles from the sand layer upstream of the barrier, yet provide optimal resistance against backward erosion piping. This means the particles in the barrier should be large, thus difficult to transport, and the barrier should have a high permeability in order to reduce the local hydraulic gradient inside the barrier. However, transport of the fine sand upstream of the barrier into the barrier, may result in less permeable filter cake just inside the barrier. Therefore, a column set-up was designed and experiments were conducted with various sand types, using the same materials as used in small-scale and medium-scale backward erosion piping experiments with at CSB. The aim was to compare the results of different tests and check if the criterion for the formation of a filter cake is the same as the well-known filter rules. Consequently, this paper presents the results of different column experiments. These materials were carefully selected and fulfilled the selected filter rules but one soil composition caused the filter cake formation. This indicates that to avoid filter cake formation for the conditions tested stricter rules apply than the filter rules considered here. More... »

PAGES

211-220

Book

TITLE

Internal Erosion in Earthdams, Dikes and Levees

ISBN

978-3-319-99422-2
978-3-319-99423-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-99423-9_20

DOI

http://dx.doi.org/10.1007/978-3-319-99423-9_20

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106466851


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