Bayesian calibration of thermodynamic parameters for geochemical speciation modeling of cementitious materials

Chemical equilibrium modeling of cementitious materials requires aqueous–solid equilibrium constants of the controlling mineral phases (Ksp) and the available concentrations of primary components. Inherent randomness of the input and model parameters, experimental measurement error, the assumptions and approximations required for numerical simulation, and inadequate knowledge of the chemical process contribute to uncertainty in model prediction. A numerical simulation framework is developed in this paper to assess uncertainty in Ksp values used in geochemical speciation models. A Bayesian statistical method is used in combination with an efficient, adaptive Metropolis sampling technique to develop probability density functions for Ksp values. One set of leaching experimental observations is used for calibration and another set is used for comparison to evaluate the applicability of the approach. The estimated probability distributions of Ksp values can be used in Monte Carlo simulation to assess uncertainty in the behavior of aqueous–solid partitioning of constituents in cement-based materials.