Mesoscale modelling of concrete – A review of geometry generation, placing algorithms, constitutive relations and applications

Thilakarathna, P.S.M.; Baduge, Shanaka Kristombu; Mendis, Priyan; Vimonsatit, Vanissorn; Lee, Hyuk

doi:10.1016/j.engfracmech.2020.106974

Cited by 140 publications

(47 citation statements)

References 200 publications

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“…There are numerous methods to model aggregate geometry in mesoscale models and 3D scanning is one of the main techniques. X-ray computed tomography (XCT) scanning is a widely used 3D scanning method to scan aggregates and concrete samples to generate the mesostructure of concrete (Huang et al 2015;Liu et al 2018a;Ren et al 2015;Shuguang and Qingbin 2015;Thilakarathna et al 2020b). However, it is time-consuming and costly to scan aggregate particles using XCT (Anochie-Boateng et al 2013) and XCT has a strict safety and radiation monitoring specifications (Anochie-Boateng et al 2012).…”

Section: D Scanning Of Aggregate Geometriesmentioning

confidence: 99%

“…Previous researchers have used various algorithms to distribute the aggregates inside the bounding geometry as reviewed in (Thilakarathna et al 2020b). However, most of the algorithms are for parameterized aggregate shapes such as spheres (Shahbeyk et al 2011;Wriggers and Moftah 2006), ellipsoids (Häfner et al 2003), convex polyhedrons (Zhou et al…”

Section: Placing Algorithms For Scanned Aggregatesmentioning

confidence: 99%

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Aggregate Geometry Generation Method Using a Structured Light 3D Scanner, Spherical Harmonics–Based Geometry Reconstruction, and Placing Algorithms for Mesoscale Modeling of Concrete

Thilakarathna

Baduge

Mendis

et al. 2021

J. Mater. Civ. Eng.

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Mesoscale numerical modelling is an effective method of representing concrete as a three-phase material. Accurate aggregate geometry representation is an important aspect in numerical mesoscale modelling of concrete to predict mechanical properties as well as the damage initiation and fracture propagation. In this paper, a novel approach of 3D scanning of aggregates P.S.M Thilakarathna would like to thank The University of Melbourne, Australia for providing Melbourne Graduate Research Scholarship Award.

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Section: D Scanning Of Aggregate Geometriesmentioning

confidence: 99%

Section: Placing Algorithms For Scanned Aggregatesmentioning

confidence: 99%

Aggregate Geometry Generation Method Using a Structured Light 3D Scanner, Spherical Harmonics–Based Geometry Reconstruction, and Placing Algorithms for Mesoscale Modeling of Concrete

Thilakarathna

Baduge

Mendis

et al. 2021

J. Mater. Civ. Eng.

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“…Forti et al (2020) investigate failure behavior using a model that combines damage and plasticity on mortars for compressive softening, and cohesive fracture on interface elements for tensile softening and cracking. For an extensive review of geometry generation, particle distribution algorithm, constitutive relations and applications, the reader is referred to Thilakarathna et al (2020). For mesoscale models and material behavior, the reader is referred to Caballero et al (2006), Häfner et al (2006), Wriggers and Moftah (2006), Kim and Al-Rub (2011), Du et al (2014), Yılmaz and Molinari (2017), Zhou et al (2017), Zhang et al (2018), Li et al (2019), Tang et al (2019) and Meng et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Modeling Particles Elements in Damaged Reinforced Concrete Structures

Ramos

Carrazedo

Paccola

2021

Lat. Am. j. solids struct.

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In this paper, we introduce a finite element mesoscale modeling of damaged concrete structures, based on nodal positions. The mesoscale modeling consists of particle and fiber finite elements embedded in matrix finite elements. While the matrix elements represent the cement matrix, particle elements are used to simulate the coarse aggregates and fiber elements are used for reinforcement rebars. The embedded theory is used to immerse the reinforcement (both particle and fiber elements) without increasing the total number of degrees of freedom. This strategy does not require nodal coincidence, allowing randomly distribute the coarse aggregates. The materials nonlinear behavior is considered by a scalar damage model for the cement matrix and coarse aggregates, and one-dimensional elastoplastic model is used for the steel rebars. Four examples are presented, with good correlation between numerical and experimental results. It is shown that structures simulated with particulate elements could endure higher loads for the same displacement, although the maximum force is obtained in models without inclusion of particle elements.

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“…It provides high resolution of the real aggregate distributions, while the result represents only one specific specimen that is captured. The parametrization approach can be the more applicable method to generate various mesostructure models by using parametrical algorithms [6]. MCM by the parameterization modeling approach includes two approaches; generation of the random shape aggregates, referred to as random shape aggregate model (RSAM), and the random placement of the simplified aggregates (e.g., circle) into the sample domain [7].…”

Section: Introductionmentioning

confidence: 99%

Image Analysis and Functional Data Clustering for Random Shape Aggregate Models

et al. 2020

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This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.

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Mesoscale modelling of concrete – A review of geometry generation, placing algorithms, constitutive relations and applications

Cited by 140 publications

References 200 publications

Aggregate Geometry Generation Method Using a Structured Light 3D Scanner, Spherical Harmonics–Based Geometry Reconstruction, and Placing Algorithms for Mesoscale Modeling of Concrete

Aggregate Geometry Generation Method Using a Structured Light 3D Scanner, Spherical Harmonics–Based Geometry Reconstruction, and Placing Algorithms for Mesoscale Modeling of Concrete

Modeling Particles Elements in Damaged Reinforced Concrete Structures

Image Analysis and Functional Data Clustering for Random Shape Aggregate Models

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