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@PHDTHESIS{Neumann:760437,
author = {Neumann, Johannes Matthias},
othercontributors = {Simon, Jaan-Willem and Reese, Stefanie and Steeb, Holger},
title = {{M}esomechanical modelling of asphalt},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2019-04184},
pages = {1 Online-Ressource (xxiv, 135 Seiten) : Illustrationen},
year = {2018},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2019; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2018},
abstract = {Asphalt is one of the oldest construction materials known
to mankind. Unfortunately, it is also one the most complex
materials. Asphalt exhibits elastic, viscous and plastic
properties. Furthermore, it damages and shows significant
ageing over the course of a few decades. These properties
are inherited mostly from the bituminous binder. However,
asphalt is actually a random heterogeneous material
consisting of mineral aggregate glued together with bitumen.
Mixed together, asphalt acquires all the properties of a
true composite material: stiffening and strengthening,
localisation, scale dependency, and many more. The present
work deals with the modelling of asphalt used in road
construction. Due to its complexity, asphalt is difficult to
model as a bulk material on the macroscale. The rationale is
to develop a mesoscale model of asphalt within a
computational mechanics multiscale framework. The mechanical
field equations are solved using the finite element
method. Here, asphalt is separated into two components,
namely asphalt mortar and coarse aggregate. This divide and
conquer approach leads to simpler submodels, and has the
additional benefit of modularity. This cumulative thesis
starts off with background information about the history of
asphalt, its constituents, mechanical properties and design
(section 1).Following the idea of modularity, the first
publication (section 2) establishes the multi scale
framework in general. A first method to generate geometries
of random heterogeneous angular particle systems based on
Voronoi tessellation is developed (section 2.3). Tetrahedral
finite element meshes are generated (section 2.3.3) and a
strain driven first order homogenisation method is employed
to compute macroscale material parameters (section 2.5). So
far, material modelling resorts to linear elasticity
(section 2.4).The second publication (section 3) is
specialising almost every aspect of the previous publication
w.r.t. road engineering. Material modelling is based on
linear viscoelastic data obtained by dynamic shear
rheometry. The generalised Maxwell model is used (section
3.3). Geometry generation is extended to represent particle
size distributions of several real mixtures (section 3.4).
The meshing procedure does now use coarse-graining for
volumes where high accuracy is not needed (section 3.5.2).
Upscaling from meso- to macroscale is performed in time
domain at a temperature of 20°C and a frequency of 1 Hz
(section 3.5). At this stage, validation against
experimental mixdata becomes possible. The realm of
computational mechanics is somewhat left in the third
publication (section 4), which concentrates on the analysis
of irregular polyhedra instead. Particular, the ambiguous
notion of “size” for non-spherical particles is
assessed. Theme thodology is based on principal component
analysis of the inertia tensor using discrete computational
geometry. This is used as a basis to propose estimators for
sieve size (section 4.3). Several shape descriptors are
introduced and compared between data from X-Ray CT scanning
(section 4.3.5) and synthetic data (section 4.3.6). With
suitable datasets at hand, the quality of sieve size
estimation as a function of sphericity is investigated
(section 4.5.2 and section 4.5.3). Additional
investigations, which are mostly unpublished yet, are
presented in section 5. Unfortunately, some shortcomings
have been revealed after publication of the three articles,
which are also adressed in this chapter.},
cin = {311510},
ddc = {624},
cid = {$I:(DE-82)311510_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2019-04184},
url = {https://publications.rwth-aachen.de/record/760437},
}
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