Reference : POROSIMETRY BY DOUBLE-RANDOM MULTIPLE TREE STRUCTURING IN VIRTUAL CONCRETE
Scientific journals : Article
Engineering, computing & technology : Civil engineering
http://hdl.handle.net/2268/124958
POROSIMETRY BY DOUBLE-RANDOM MULTIPLE TREE STRUCTURING IN VIRTUAL CONCRETE
English
Stroeven, Piet [ > > ]
L.B.Le, Nghi [ > > ]
Sluys, L.J. [Delft University of Technology > > > >]
He, Huan [Université de Liège - ULg > Département Argenco : Secteur GeMMe > Matériaux de construction non métalliques du génie civil >]
Jun-2012
Image Analysis and Stereology
International Society for Stereology
31
1
55-63
Yes
International
1580-3139
Ljubljana
Slovenia
[en] DEM ; pore connectivity ; porosimetry ; star volume ; virtual concrete
[en] Two different porosimetry methods are presented in two successive papers. Inspiration for the development came from the rapidly-exploring random tree (RRT) approach used in robotics. The novel methods are applied to virtual cementitious materials produced by a modern concurrent algorithm-based discrete element modeling system, HADES. This would render possible realistically simulating all aspects of particulate matter that influence structure-sensitive features of the pore network structure in maturing concrete, namely size, shape and dispersion of aggregate and cement particles. Pore space is a complex tortuous entity. Practical methods conventionally applied for assessment of pore size distribution may fail or present biased information. Among them, mercury intrusion porosimetry and 2D quantitative image analysis are popular. The mathematical morphology operator “opening” can be applied to sections and even provide 3D information on pore size distribution, provided isotropy is guaranteed. Unfortunately, aggregate grain surfaces lead to pore anisotropy. The presented methods allow exploration of pore space in the virtual material, after which pore size distribution is derived from star volume measurements. In addition to size of pores their continuity is of crucial importance for durability estimation. Double-random multiple tree structuring (DRaMuTS), presented herein, and random node structuring (RaNoS) provide such information. The latter method will be introduced in a next issue of Image Anal Stereol.
http://hdl.handle.net/2268/124958
10.5566/ias.v31.p.55-63

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