Probabilistic prediction model of concrete carbonation depth considering the influence of multiple factors

Abstract In order to realize the probability prediction of concrete carbonation depth, this paper proposed a probabilistic prediction model of carbonation depth based on 433 sets of existing experimental data. First, considering the influence of water‐binder ratio, fly ash content, stress level, and carbonation time, a deterministic prediction model of carbonation depth was established based on stepwise regression method. Then, based on the established deterministic prediction model, considering the influence of aleatory and epistemic uncertainties, a probabilistic prediction model of carbonation depth was established, and the posterior distributions of the parameters in the probabilistic prediction model were updated iteratively by using Bayesian theory and Markov chain Monte Carlo method, and the influence of standard deviation of prior distribution of the parameters in probabilistic prediction model on their posterior distributions was discussed. Finally, the prediction accuracy of the probabilistic prediction model was verified by comparing with another 127 sets of existing experimental data and the predicted values of the deterministic prediction model, and the effect of the standard deviations of prior distribution of the parameters on the prediction accuracy of the probabilistic prediction model was analyzed. The results showed that the prediction accuracy of the proposed probabilistic prediction model of carbonation depth after three updates was higher than that of the deterministic prediction model. When the standard deviation of prior distributions of the parameters in probabilistic prediction model was 100, the proposed probabilistic prediction model after three updates had the highest prediction accuracy. Under the tensile stress state of concrete, the prediction accuracy of the probabilistic prediction model was improved by 26.76% compared with the deterministic prediction model, and when the concrete under compressive stress state, the prediction accuracy of the probabilistic prediction model was 44.07% higher than that of the deterministic prediction model.