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A multiscale framework for the prediction of concrete self-desiccation

Pathirage, M., Bentz, D.P., Di Luzio, G., Masoero, E. and Cusatis, G. 2018. A multiscale framework for the prediction of concrete self-desiccation. Presented at: Computational Modelling of Concrete and Concrete Structures (EURO-C 2018), 26 Feb-1 Mar 2018. Published in: Meschke, Gunther, Pichler, Bernhard and Rots, Jan G. eds. Computational Modelling of Concrete Structures: Proceedings of the Conference on Computational Modelling of Concrete and Concrete Structures (EURO-C 2018), February 26 - March 1, 2018, Bad Hofgastein, Austria. Taylor and Francis, pp. 203-208. 10.1201/9781315182964-25

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Cement hydration in concrete and mortar has been studied thoroughly over the past 50 years. To fully understand hydration in concrete and predict the evolution of the hygral, thermal, and mechanical properties at the structural level, one needs to studynot only the reaction kinetics but also the development of the microstructure. Many models have been developed for this purpose, some of them looking only at the micro-scale or at the macro-scale and others tackling the fundamental nature of the issue, which can be qualified as a multiscale problem. This paper proposes a novel approach that consists of combining a cement hydration model at the microstructural level, the CEMHYD3D model, with a macroscopic hygro-thermo-chemical model, the HTC model. The coupling is performed by postprocessing the output of the CEMHYD3D model, in particular with reference to cement hydration degree, silica fume reaction degree, and amounts of evaporable water and chemically bound water in order to identify through a curve fitting routine the parameters of the HTC formulation. This approach allows the possibility of predicting concrete behavior at multiple scales based on the actual chemical and microstructural evolution, thus enhancing the capabilities of the so-called HTC-CEMHYD3D model. This paper focuses on 1) introducing the concepts behind the formulation of self-desiccation and 2) demonstrating the predictive capabilities of the coupled model using some available experimental data.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Engineering
Publisher: Taylor and Francis
Date of First Compliant Deposit: 29 September 2021
Last Modified: 06 Jan 2024 02:09

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