Pore structure characteristics of cementitious materials play a critical role in the transport properties of concrete structures. This paper develops a novel framework for modeling chloride penetration in concrete, considering the pore structure-dependent model parameters. In the framework, a multi-scale transport model was derived by linking the chloride diffusivities with pore size distributions (PSDs). Based on the three-dimensional (3D) microstructure generated by “porous growth” and “hard core-soft shell” methods, two sub-models were computationally developed for determining the multi-modal PSDs and pore size-related chloride diffusivities. The predicted results by these series of models were compared with corresponding experimental data. The results indicated that by adopting pore size-related diffusivities, even if the total porosities were the same, the proposed multi-scale chloride transport model could better capture the effect of different PSDs on chloride penetration profiles, while the model without pore structure-depended parameters would ignore the differences. Compared with the reference transport models, which adopt averaged chloride diffusivities, the chloride penetration depths predicted by the proposed multi-scale model are in better agreement with experimental data, with 10%–25% reduced prediction error. This multi-scale transport model is hoped to provide a novel computational approach on 3D microstructure generation and better reveal the underlying mechanism of the chloride penetration process in concrete from a microscopic perspective.@en

This paper develops a novel lattice diffusive model to quantitatively study the chloride diffusion coefficient in unsaturated cementitious materials, in which the pore voxels are redistributed to make a better representation of a real microstructure of hardened cement paste. Considering the hierarchical microstructure and different drying-wetting cycles, water distributions in multiscale pore structures are modelled and the structure characteristics of water-filled pores, including water connectivity, water tortuosity and effective porosity, are computationally extracted based on that. A lattice diffusion network is established to predict relative chloride diffusion coefficient by combining the effect of both water saturation degree and pore structure characteristics. The predicted results are validated against experimental data, and a concise analytical equation is proposed to predict the relative chloride diffusion coefficient. The equation indicated that the relative chloride diffusion coefficient is proportional to water connectivity but inversely proportional to the square of water tortuosity. Besides, the lattice model's quantitative results reveal that the water connectivity and water tortuosity are highly related to pre-water loading processes, and influenced by the gel pore fraction, which in turn will affect the relative chloride diffusion coefficient. Compared with existing equations and non-redistributed models, the present model could improve the prediction accuracy significantly.

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