# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 1 Introduction
Recently, several researchers have utilized [PERSON] theorem in their analysis to reflect field conditions and to estimate future environmental conditions [2; 5; 16]. [PERSON] et al. estimated the corrosion resistivity of reinforced concrete structures exposed to chloride attacks, and their remaining service life, using [PERSON] theorem [16]. They estimated the remaining service life of seashore RC structures, which are mainly deteriorated by chloride attack, without considering carbonation effects in their durability analysis. [PERSON] et al. estimated the carbonation progress of concrete structures and also employed a Bayesian approach to better consider the effect of changing environmental conditions, such as sunlight and wind speed, in the analysis [5]. However, their estimation approach only considered experimental data for carbonation and employed climate data in their Bayesian approach. Thus, the approach might be available in the design stage but not for maintaining existing structures. [PERSON] et al. also employed [PERSON] theorem to reflect the uncertainty of the field condition while executing an assessment of an existing bridge [17]. They assessed the deterioration of a concrete bridge using basic design data and field surveys, such as concrete strength and rebar size. Although they estimated the deterioration of a concrete structure considering on-site data, their approach does not consider carbonation progress in the field. Similarly, [PERSON] et al. proposed a bridge element deterioration curve for estimating the residual life of bridges based on visual inspection with Bayesian theory [18]. The approach might be used to effectively manage an existing bridge from the proposed degradation curve, but cannot predict the carbonation of concrete structures before corrosion initiation. [PERSON] et al. compared the descriptive rules in Euro code with previous prescriptive rules in the study and a proposed probability design approach using [PERSON] theorem [2]. In their approach, they investigated various field inspection data in Europe and updated the carbonation model which was initially predicted by prescriptive rules suggested by the code. Although the study proposed a probability-based design approach, it is not appropriate to precisely estimate carbonation propagation in existing structures. In this study, an approach predicting carbonation progress was developed using Bayesian statistics with previous field inspection data. First, an initial prediction of the carbonation of a concrete structure and its remaining service life was introduced based on [PERSON]'s first law and Latin hypercube sampling (LHS) with existing statistic data. The revised prediction of carbonation progress and remaining service life was then proposed using Bayesian statistics with previous on-site data. In the prediction approach, important design variables including CO2 diffusion coefficients, atmospheric CO2 concentrations, quantity of CO2 absorbed, and cement hydration for the initial carbonation velocity coefficient were also considered to reduce uncertainties that might arise in early stages. The proposed approach applies to three bridges located in different areas such as land, river, and sea in South Korea and considered on-site data using Bayesian theory. The results of the developed prediction approach represent a suitable estimation of carbonation progress for each bridge, so it is expected to effectively predict concrete carbonation for maintaining existing bridges in the field. Thus, the carbonation prediction approach can also be used to make a flexible and proper decision for the operation and maintenance of deteriorated concrete structures in use.