###### Abstract  
Reinforfored concrete slab (RCS) bridges deteriorate because of exposure to environmental factors over time, resulting in reduced durability. Particularly, the carbonation of RCS bridges corrodes the rebars and reduces the strength. However, carbonation models derived from short-term experiments exhibit low reliability with respect to existing bridges. Therefore, a long short-term memory (LSTM)-based methodology was developed in this study for generating carbonation models using existing bridge inspection reports. The proposed methodology trains the LSTM model by combining data extracted from reports and local environmental data. The learning process uses padding and masking methods to consider the history of environmental data. A case study was performed to validate the proposed method in three different regions of Korea. The results verified that the coefficient of determination of the proposed method was higher than those of the existing carbonation models and other regression analyses. Therefore, the developed methodology can be used for predicting regional carbonation models using the data from existing bridges. concrete carbonation; long short-term memory (LSTM); reinforced concrete slab (RCS) bridge; bridge deterioration; carbonation model +
Footnote †: journal: Applied  
doi: 10.3390/acm/10.3390/acm/10. to regress the short-term carbonation of concrete within one year. The short-term carbonation model is most affected by the mixing ratio and curing time of the concrete [3]. However, field investigations of the natural environment remain scarce despite numerous experimental and theoretical studies. In general, three limitations are observed in applying the carbonation model to actual bridges. First, the actual reinforced concrete bridge over 10 years is significantly affected by environmental variables from a long-term perspective. However, the experimental environment estimates the weights for the environmental parameters because it exposes the concrete only for a short period. The low weighting of environmental variables can deteriorate the performance of long-term carbonation models. Second, most of the design data for bridges older than 30 years have been lost. Moreover, the mixing and curing periods of the concrete set in the design stage change owing to errors during the actual construction. These problems prevent the calculation of constants of several developed carbonation models, thereby reducing their usefulness. Third, environmental conditions change because of continuous changes in climate. The increase in the average temperature caused by global warming and the increasing trend of atmospheric CO\({}_{2}\) concentration (278 to 400 ppm during the industrial period [4]) may accelerate the carbonation of cement and concrete [5; 6]. Owing to these limitations, establishing a mathematical calculation model to accurately reflect the various factors of complex carbonation is difficult. Therefore, a novel approach that differs from the existing theories is required. Artificial neural network (ANN) algorithms have been applied to solve several complex problems in concrete technology [7; 8; 9; 10; 11; 12; 13], and ANN-based models are widely used for estimating specific carbonation depths [14; 15; 16; 17; 18; 19]. Although most studies consider certain influencing parameters, environmental conditions are not considered because of continuous changes in climate [20; 21]. ANN models including climate change will produce highly accurate carbonation models, but further studies are needed [22; 23]. Therefore, this study aims to develop an ANN-based methodology to predict the long-term carbonation model of field bridges considering climate change. The generated carbonation model considers climate change using data extracted from inspection reports. The dataset was constructed from the carbonation data of slabs, girders, piers, and abutments, which are major members of reinforced concrete slab (RCS) bridges in Korea.