# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 1 Introduction  
Carbonation is one of the critical durability issues in concrete structures in terms of structural integrity and safety. Carbonation may cause fatal deterioration and corrosion of steel reinforcement of concrete structures. Furthermore, increasing atmospheric pollution can accelerate the deterioration of concrete structures, so a more effective carbonation prediction model in the field is needed to securely maintain existing structures. Thus, many researchers have conducted a considerable amount of studies to predict the carbonation of concrete structures. However, it is still challenging to predict carbonation in concrete structures because the carbonation progress can be affected by various conditions such as curing time and quality, sheltering, CO\({}_{2}\) concentration in the atmosphere, air voids included in the concrete, the concrete mix, binder and aggregate type, existence of cracks, and concrete finishing materials [1; 2; 3; 4; 5; 6; 7; 8; 9]. Most studies for proposing carbonation prediction models are based on their laboratory experiments with accelerated testing, but some review articles indicated that the accelerated test has limitations for estimation of the concrete carbonation in the fields [6; 10; 11]. Furthermore, existing research still shows conflicting findings [10], so a field-oriented carbonation prediction approach needsto be developed. Recently, several researchers have employed field inspection data to effectively predict concrete carbonation. [PERSON] et al. have studied the carbonation progress with respect to concrete strength and concrete cover depth [11]. They analyzed 436 data sets from 80 harbor facilities and compared them with existing prediction models. Although Kishitani and RILEM 130 models can predict the carbonation progress within an acceptable safety margin, their field data showed large variation from the prediction models. Also, [PERSON] et al. proposed the particle swarm optimization algorithm based on the BP neural network to effectively predict carbonation in their field [12]. The algorithm reduced the learning time and increased the accuracy of the carbonation estimation, but only considered cement content, w/c ratio, and relative humidity. Thus, a comprehensive analysis reflecting various conditions is required to accurately predict carbonation progress in the field. [PERSON] et al. employed field inspection data from nine different buildings and predicted carbonation using an adaptive neurofuzzy inference system which is an AI algorithm [13]. They considered chloride attack, compressive strength, and crack width as input variables in the algorithm. Although the approach proposed a carbonation model for the target buildings with relatively good accuracy, more information was required for a precise prediction. To consider the regional and environmental effects of the carbonation, [PERSON] et al. investigated the carbonation progress of 25 concrete structures located in the semitropical region of India [14]. They examined carbonation with respect to different concrete covers, structural ages, and compressive strengths and proposed a carbonation velocity of RC structures located in the semitropical regions. Additionally, [PERSON] proposed a carbonation prediction model reflecting natural carbonation in South Africa after examining 163 field survey data sets for 10 years [15]. He compared his model with fib model code resulting in similar trends with the code. However, both prediction models are only available to estimate a representative carbonation model for each area where the on-site data was obtained. Furthermore, the models might be difficult to consider a carbonation progress to maintain a specific concrete structure under different environmental and construction conditions. To better consider various environmental conditions in the field, many researchers have introduced a probability-based approach for durability analysis [16; 1; 2; 3; 4; 5].