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

• Published in: Cement and concrete research Vol. 42; no. 7; pp. 889 - 902
• Main Authors: Sarkar, S., Kosson, D.S., Mahadevan, S., Meeussen, J.C.L., der Sloot, H. van, Arnold, J.R., Brown, K.G.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-07-2012; Elsevier
• Subjects: Applied sciences; APPROXIMATIONS; Bayesian analysis; Bayesian calibration; BUILDING MATERIALS; Buildings. Public works; CALCULATION METHODS; CALIBRATION; Cement (D); Cement concrete constituents; CEMENTS; CHEMISTRY; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Computer simulation; COMPUTERIZED SIMULATION; DISSOLUTION; DISTRIBUTION; EQUILIBRIUM; Exact sciences and technology; FORECASTING; FUNCTIONS; Geochemical speciation; GEOCHEMISTRY; LEACHING; MATERIALS; MATERIALS SCIENCE; Mathematical models; MINERALS; MONTE CARLO METHOD; Monte Carlo methods; PROBABILITY; PROBABILITY DENSITY FUNCTIONS; Properties and test methods; Properties of anhydrous and hydrated cement, test methods; SAMPLING; SEPARATION PROCESSES; SIMULATION; Speciation; Thermodynamic parameters; Uncertainty; Bayesian calibration; Thermodynamic parameters; Geochemical speciation; Cement (D); Bayes estimation; Experimental data; Construction materials; Geochemistry; Concentration; Calibration; Forecast model; Modeling; Chemical equilibrium; Thermodynamic parameter; Cement; Comparative study
• ISSN: 0008-8846; 1873-3948
• DOI: 10.1016/j.cemconres.2012.02.004

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.