A statistical finite element method integrating a plurigaussian random field generator for multi-scale modelling of solute transport in concrete

Ricketts, Evan John ORCID: https://orcid.org/0000-0001-8056-070X, Freeman, Brubeck Lee, Cleall, Peter John ORCID: https://orcid.org/0000-0002-4005-5319, Jefferson, Anthony ORCID: https://orcid.org/0000-0002-2050-2521 and Kerfriden, Pierre ORCID: https://orcid.org/0000-0002-7749-3996 2023. A statistical finite element method integrating a plurigaussian random field generator for multi-scale modelling of solute transport in concrete. Transport in Porous Media 10.1007/s11242-023-01930-8

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Abstract

A new model for the multi-scale simulation of solute transport in concrete is presented. The model employs plurigaussian simulations to generate stochastic representations of concrete micro- and meso-structures. These are idealised as two-phase medium comprising mortar matrix and pores for the micro-structure, and mortar and large aggregate particles for the meso-structure. The generated micro- and meso-structures are employed in a finite element analysis for the simulation of steady-state diffusion of solutes. The results of the simulations are used to calculate effective diffusion coefficients of the two-phase micro- and meso-structures, and in turn, the effective diffusion coefficient at the macro-scale at which the concrete material is considered homogenous. Multiple micro- and meso-structures are generated to account for uncertainty at the macro-scale. In addition, the level of uncertainty in the calculated effective diffusion coefficients is quantified through a statistical analysis. The numerical predictions are validated against experimental observations concerning the diffusion of chloride through a concrete specimen, suggesting that the generated structures are representative of the pore-space and coarse aggregate seen at the micro- and meso-scales, respectively. The method also has a clear advantage over many other structural generation methods, such as packing algorithms, due to its low computational expense. The stochastic generation method has the ability to represent many complex phenomena in particulate materials, the characteristics of which may be controlled through the careful choice of intrinsic field parameters and lithotype rules.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Engineering
Funders:	EPSRC
Date of First Compliant Deposit:	10 March 2023
Date of Acceptance:	9 March 2023
Last Modified:	09 Nov 2024 22:15
URI:	https://orca.cardiff.ac.uk/id/eprint/157665

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