Mimoun, Abdulkarim and Kulasegaram, Sivakumar ORCID: https://orcid.org/0000-0002-9841-1339 2022. Simulation of self-compacting steel fibre reinforced concrete using an enhanced SPH methodology. Meschke, Gunther, Pichler, Bernhard and Rots, Jan G., eds. Computational Modelling of Concrete and Concrete Structures, Taylor and Francis, pp. 162-168. (10.1201/9781003316404-20) |
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Abstract
Accurate prediction of self-compacting fibre reinforced concrete (SCFRC) flow, passing and filling behaviour is not a trivial task, particularly in the presence of heavy reinforcement, complex formwork shapes and large size of aggregates. In this regard, complex formwork shapes and large size of aggregate can play an important role in fibre orientation and distribution during the flow of fibre reinforced self-compacting concrete and can thus significantly influence mechanical behaviour of the hardened material. Due to the nature of selfcompacting concrete mix and widely varying properties of its constituents, it is hugely challenging to understand the rheological behaviour of the concrete mix. For this reason, it is necessary to thoroughly comprehend fresh property by understanding its rheology. The quality control and accurate prediction of the SCFRC rheology are crucial for the success of its production. A three-dimensional meshless smoothed particle hydrodynamics (SPH) computational approach, treating the SCFRC mix as a non-Newtonian Bingham fluid constitutive model has been coupled with the Lagrangian momentum and continuity equations to simulate the flow. The aim of this numerical simulation is to investigate the capabilities of the SPH methodology in predicting the flow and passing ability of SCFRC mixes through gaps in reinforcing bars. To confirm that the concrete mixes flow homogeneously, the distribution and orientation of steel fibres in the mixes have been simulated and compared against observations made in the laboratory experiments. It is revealed that the simulated flow behaviour of SCFRC compares well with results obtained in the laboratory tests.
Item Type: | Book Section |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Publisher: | Taylor and Francis |
ISBN: | 9781003316404 |
Date of First Compliant Deposit: | 13 July 2023 |
Last Modified: | 13 Jul 2023 11:13 |
URI: | https://orca.cardiff.ac.uk/id/eprint/160915 |
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