Effective and Apparent Diffusion Coefficients of Chloride Ions and Chloride Binding Kinetics Parameters in Mortars:...

Area of Science:

• Materials Science
• Civil Engineering
• Chemical Engineering

Background:

• Chloride ingress is a major cause of concrete degradation, impacting infrastructure longevity.
• Understanding chloride transport and binding mechanisms in cementitious materials is critical for predicting material performance.
• Existing models often simplify the complex interactions between diffusion and chemical binding of chlorides.

Purpose of the Study:

• To develop and validate a non-equilibrium diffusion-reaction model for chloride transport in cementitious materials.
• To determine effective chloride diffusion coefficients and binding parameters using an inverse method.
• To compare the proposed model with classical diffusion models and assess the role of chloride binding.

Main Methods:

• A non-equilibrium diffusion-reaction model was formulated for chloride transport and binding.
• The finite element method was employed for the numerical solution of the non-linear problem.
• An inverse method, using experimental chloride concentration data, was used to determine model parameters.
• Investigations were conducted on ordinary Portland cement and blast furnace cement.

Main Results:

• The proposed model successfully describes chloride transport and binding in different cement types.
• Effective diffusion coefficients and binding parameters were determined for the studied cements.
• The study highlights the significant role of chloride binding in overall chloride transport.
• Comparisons showed limitations of classical diffusion models in representing these phenomena.

Conclusions:

• The developed diffusion-reaction model provides a more accurate representation of chloride behavior in cementitious materials.
• The methodology allows for the determination of key parameters influencing concrete structure longevity.
• The model's framework is adaptable for incorporating additional factors like multiple ions and electrostatic interactions for enhanced predictive accuracy.