## 1 Introduction  
Concrete is one of the most widely used and versatile building materials in the world. However, the carbonation of concrete poses a significant threat to its structural integrity. This process occurs when the alkali in the concrete structure reacts with carbon dioxide (CO\({}_{2}\)) in the environment, resulting in the production of calcium carbonate (CaCO\({}_{3}\)). As a result, the basicity of the concrete environment is reduced, and its original protective effect for reinforcement is compromised. This is the primary reason for steel bar corrosion in concrete [1]. In reinforced concrete structures, corrosion of the reinforcement can cause the corroded products to expand, generating pressure on the surrounding concrete. This pressure can eventually lead to the deterioration of the concrete's durability. Thus, understanding the carbonation process and its impact on concrete structures is crucial for ensuring their longevity and safe use.  
Concrete carbonation is a complex physicochemical process involving numerous unknown factors. Due to the random nature of building environments and the uncertainties regarding concrete quality, carbonation depth in concrete structures is highly variable and difficult to predict. Even in identical environmental conditions, carbonation depth can be vastly different for concrete structures of the same strength [1]. As a result, understanding the underlying mechanisms of carbonation and its impact on concrete structures is critical to ensuring the longevity and safety of these structures.  
Carbonation modeling and the prediction of carbonation depth are essential for evaluating the durability of reinforced concrete structures [2]. Over the past few decades, numerous experts and scholars have analyzed concrete carbonation theory and experimentation in order to develop prediction models. [PERSON] et al. developed a calculation model for predicting carbonation depth based on carbonation theory and experimental results [3], while [PERSON] et al. proposed a mathematical approach to predict carbonation depth based on accelerated tests [4]. However, it is important to consider environmental conditions as influencing factors in carbonation rates. [PERSON] et al. evaluated the influence of environmental conditions on the carbonation process in concrete structures, while [PERSON] et al. presented the \"self-terminated carbonation model,\" which takes into account these factors [5; 6].  
[PERSON] et al. [7] established predictive modeling for the depth of carbonation of iron tailings powder concrete by introducing various influential factors. The carbonation process is very complex and is influenced by many factors, and there is interaction between biochemical factors, such as fungi and mildew; these factors affect the carbonation reaction. Modern machine learning algorithms can construct forecasting models with effects from empirical data, which may then be used to forecast features in future investigations [8].  
Given that the carbonation process is complex and influenced by multiple factors, including interactions with biochemical factors such as fungi and mildew, artificial intelligence can provide new research avenues for carbonation depth forecasting. Neural network models, such as the adaptive neuro-fuzzy inference system (ANFIS), have been applied to carbonation depth forecasting with high prediction precision [9; 10; 11]. However, the accuracy of these models depends on the quality of the input data. Principal component analysis (PCA), a popular algorithm for solving multicollinearity problems, can optimize artificial neural network models by eliminating redundant data.  
This paper presents a novel approach, the PCA-ANFIS model, for predicting carbonation depth in concrete structures. The model combines PCA with ANFIS to eliminate redundant data and optimize the input variables. The practical engineering example presented in this paper demonstrates the superior performance of the proposed PCA-ANFIS model.