Fundamental modeling and experimental investigation of concrete carbonation

TitleFundamental modeling and experimental investigation of concrete carbonation
Publication TypeJournal Article
Year of Publication1991
Papadakis VG, Vayenas CG, Fardis MN
JournalACI Materials Journal

The physicochemical processes in concrete carbonation are presented and modeled mathematically. These processes include the diffusion of CO2 in the gas phase of concrete pores, its dissolution of solid Ca(OH)2 in pore water, its ultimate reaction with the dissolved CO2, and the reaction of CO2 with SH and with the yet unhydrated C3S and C2S. In addition, the parallel processes of production of materials susceptible to carbonation during the hydration of cement, and of reduction of concrete porosity during hydration and carbonation, are included in the model. The mathematical model yields a complex nonlinear system of differential equations in space and time, and must be solved numerically for the unknown concentrations of the materials involved. For the usual range of parameters, certain simplifying assumptions can be made, which lead to the formation of a carbonation front, separating completely carbonated regions from the ones in which carbonation has not yet started. For one-dimensional geometry, the evolution of the front location with time is given by a simple analytical expression, in terms of the effective diffusivity of CO2 in carbonated concrete and of the total molar concentration of CaO in concrete, in the form of carbonatable materials. The reliability of the analytical expression is verified by comparing its predictions with the results of accelerated carbonation tests conducted in a test chamber on several mixes of concrete or mortar. The analytical expression is shown to predict in good approximation the dependence of carbonation depth on time, water-cement ratio, aggregate-cement ratio, atmospheric concentration of CO2, ambient temperature, and relative humidity (at least for humidities above 50 percent). The proposed model can also predict the test results of previous investigators for exposure to natural or increased concentrations of CO2.

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