Prediction of concrete carbonation based on the inverse Gaussian process and Bayesian method

Abstract

Concrete carbonation is one of the major factors causing the deterioration of reinforced concrete structures. Therefore, accurately predicting the carbonation depth is of great significance in safety assessments of structures. The aim of this study was to develop a method to predict carbonation behaviour by incorporating multi-source information using the Bayesian method. First, the inverse Gaussian process was used to model the evolution of carbonation depth; this captured the temporal variability and the monotonicity of the deterioration phenomenon very well. Then, a proper prior for the model was determined using knowledge from the existing empirical carbonation model. To fuse the accelerated carbonation data and field inspection data, Bayesian inference was performed to update the posterior distributions of the model parameters by the Gibbs sampling technique. A practical example case was used to illustrate the validity and accuracy of the proposed approach.