Numerical simulation on diffusion–reaction behavior of concrete under sulfate–chloride coupled attack

Qi, Dong; Haorui, Zheng; Lijuan, Zhang; Guowen, Sun; Haitao, Yang; Yifan, Li

doi:10.1016/j.conbuildmat.2023.133237

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Meanwhile, the reliability of the model was validated by the measurements of chloride concentration in concrete exposed to the marine environment long term that were reported in the literature. Qi et al [11] proposed a variable coefficient transport model of sulfate-chloride in concrete based on the law of conservation of mass, Fick's second law and the theory of porous media, which was verified by comparison with the experimental results. Shazali et al [12] evaluated the effect of chloride binding in terms of the isotherm formulations on time-to-corrosion activation resulting from transport of chloride in saturated concrete through nonlinear finite element analyses.…”

Section: Introductionmentioning

confidence: 91%

Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

Zhou,

Tao,

Zhang

et al. 2023

Sustainability

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The durability degradation of reinforced concrete was mainly caused by chloride ingress. Former studies have used component parameters of concrete to predict chloride diffusion by machine learning (ML), but the relationship between microstructure and macroparameter of concrete need to be further clarified. In this study, multi-layer perceptron (MLP) and support vector machine (SVM) were used to establish the prediction model for chloride diffusion coefficient in concrete, especially for the solid waste concrete. A database of concrete pore parameters and chloride diffusion coefficients was generated by the algorithm based on the Gaussian mixture model (GMM-VSG). It is shown that both MLP and SVM could make good predictions, in which the data using the normalization preprocessing method was more suitable for the MLP model, and the data using the standardization preprocessing method was more adapted to the SVM model.

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Section: Introductionmentioning

confidence: 91%

Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

Zhou,

Tao,

Zhang

et al. 2023

Sustainability

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Effects of different cooling methods on the resistance to chloride ion penetration in concrete produced with slag-modified cement and exposed to elevated temperatures

Oliveira,

Ribeiro,

Pedroti

et al. 2024

Journal of Building Engineering

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Mesoscopic Numerical Simulation of Freeze–Thaw Damage in Hydraulic Concrete

Wang,

Liu,

Liu

et al. 2024

Buildings

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To investigate the impact of freeze–thaw damage on the mechanical properties of concrete, this study utilized Python in combination with ABAQUS 2016 to generate a two-dimensional meso-scale model of concrete. Uniaxial compression tests were conducted on the concrete after freeze–thaw cycles to study the evolution of its mechanical properties. Using “relative compressive strength” as a variable, the relationships between this variable and the parameters of the freeze–thaw damage model were determined, leading to the establishment of the freeze–thaw damage model and the simulation of compressive tests on concrete after freeze–thaw cycles. This study also explored the changes in the mechanical properties of concrete with variations in ITZ parameters and coarse aggregate content. The conclusions drawn are as follows: A comparison with experimental data showed that the model ensures that the relative error of each mechanical property parameter does not exceed 7%, verifying the model’s rationality. Increasing the ratio of ITZ parameters improved the mechanical properties of the ITZ, enhancing the overall mechanical performance, but had almost no effect on the elastic modulus. Compared to ratios of 0.7 and 0.8, concrete with a ratio of 0.9 showed slower rates of decrease in compressive strength and elastic modulus and slower rates of increase in peak compressive strain after freeze–thaw cycles. The increase in coarse aggregate content had a similar effect on the strength and freeze–thaw resistance of concrete as the ratio of ITZ parameters. Concrete with a coarse aggregate content of 60% exhibited slower rates of change in mechanical properties after freeze–thaw cycles.

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Numerical simulation on diffusion–reaction behavior of concrete under sulfate–chloride coupled attack

Cited by 3 publications

References 38 publications

Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

Effects of different cooling methods on the resistance to chloride ion penetration in concrete produced with slag-modified cement and exposed to elevated temperatures

Mesoscopic Numerical Simulation of Freeze–Thaw Damage in Hydraulic Concrete

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