ABSTRACT
This paper aims to develop a chloride transport model of engineered cementitious composites (ECC) that can consider the influence of both exposure time and crack width. ECC specimens with crack widths of 0.1 mm, 0.2 mm and 0.3 mm were soaked into NaCl solution with periods of 30, 60, 90 and 120 days. The free chloride content profile was measured and used for the development of the transport model. Regression analysis was applied to build the time and crack width dependent models of apparent diffusion coefficient and surface chloride content. The results show that the crack width has significant influence on the free chloride concentration profile when it is above 0.2 mm and the time-dependent constant n decreases linearly with the crack width. The chloride transport model was obtained by subscribing the models of apparent diffusion coefficient and surface chloride content into the analytical solution of Fick's second law. The model was further validated with the experimental results, showing a deviation within 20%. The findings of the presented study can enhance the current understanding on the chloride transportation in ECC.
ABSTRACT
Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.
ABSTRACT
Deposition of chloride ions in the surface layer of concrete is investigated in this study. In real concrete structure, chloride ions from the service environment can penetrate into concrete and deposit in the surface layer, to form the boundary condition for further diffusion towards the interior. The deposit amount of chloride ions in the surface layer is normally a function of time, rather than a constant. In the experimental investigation, concrete specimens with different mix proportions are immersed in NaCl solution with a mass concentration of 5%, to simulate the shallow immersion condition in sea water, and the surface chloride concentrations are measured at different ages. It is found that the surface chloride concentration increases following the increasing immersion durations, and varies from a weight percentage of 0.161%-0.781% in concretes with different mix proportions. The w/c (water-to-cement ratio) influences the surface chloride concentration significantly, and the higher the w/c is, the higher the surface chloride concentration will be, at the same age. However, following the prolonging of immersion duration, the difference in surface chloride concentration induced by w/c becomes smaller and smaller. The incorporation of fly ash leads to higher surface chloride concentration. The phenomena are explained based on pore structure analyses.