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  4. Computational performance of an embedded reinforcement mesh generation method for large-scale RC simulations
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Computational performance of an embedded reinforcement mesh generation method for large-scale RC simulations

Journal
International Journal of Computational Methods
Date Issued
2015
Author(s)
Markou, George  
DOI
10.1142/S021987621550019X
Abstract
In this paper, a numerical investigation on the limits of an automatic procedure for the generation of embedded steel reinforcement inside hexahedral finite elements (FEs) is presented. In 3D detailed reinforced concrete simulations, mapping the reinforcement grid inside the concrete hexahedral FEs is performed using the end-point coordinates of the rebar reinforcement macro-elements. This procedure is computationally demanding while in cases of large-scale models, the required computational time for the reinforcement mesh generation is excessive. This research work scopes to study and present the limitations of the embedded mesh generation method that was proposed by Markou and Papadrakakis, through the use of a 64-bit operating system. The embedded mesh generation method is integrated with a filtering algorithm in order to allocate and discard relatively short embedded rebar elements that result from the arbitrary positioning of the embedded rebar macro-elements and the nonprismatic geometry of the hexahedral mesh. The computational robustness and efficiency of the integrated embedded mesh generation method are demonstrated through the analysis of three numerical models. The first two numerical models are a full-scale 2-story and a 7-story RC structures while the third model deals with a full-scale RC bridge with a trapezoidal section and a total span of 100 m. Through the third numerical implementation, the computational capacity of the integrated embedded rebar mesh generation method is investigated.
Subjects

Embedded reinforcemen...

Mentmesh generation

Large-scale models

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COMPUTATIONAL PERFORMANCE.pdf

Size

5.09 MB

Format

Adobe PDF

Checksum (MD5)

7f2304ac95200097ab3888097d6a8936

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