###### Abstract  
The widespread application of reinforced concrete structures in different environmental conditions has underscored the need for effective maintenance and repair strategies. These structures offer numerous advantages, but are not impervious to the deleterious effects of chemical deterioration. The outcomes of this research hold significant implications for the management system of reinforced concrete structures. This study proposes the utilization of a fuzzy expert system as a means of enhancing the diagnosis of chemical deterioration in reinforced concrete structures that is a valuable tool for engineers and decision-makers involved in the maintenance and repair of these structures. The fuzzy expert system serves as an intelligent tool that can incorporate various symptoms of deterioration and inspection data to improve the accuracy and reliability of the diagnostic process. By integrating these inputs, the system evaluates 21 different data points, each representing a specific aspect of deterioration, on a scale ranging from 0 to 100. This numerical representation allows for a quantification of the level of deterioration, with 0 denoting minimal deterioration and 100 indicating severe deterioration. The effectiveness of the fuzzy expert system lies in its ability to process the vast amount of data and apply fuzzy operations on 352 fuzzy rules. These rules define the relationships between the inspection data, the type of deterioration, and its extent. Through this computational process, the fuzzy expert system can provide valuable insights into 10 distinct types of chemical deterioration, facilitating a more precise and comprehensive diagnosis. The implementation of the fuzzy expert system has the potential to revolutionize the field of diagnosing chemical deterioration in reinforced concrete structures. By addressing the limitations of traditional methods, this advanced approach can significantly improve the clarity and accuracy of the diagnostic process. The ability to obtain more precise information regarding the type and extent of deterioration is vital for developing effective maintenance and repair strategies. Ultimately, the fuzzy expert system holds great promise in enhancing the overall durability and performance of reinforced concrete structures in various environments. Keywords:expert system; fuzzy logic; chemical deterioration; reinforced concrete structures +
Footnote †: journal: Applied sciences  
chemical deterioration poses a significant challenge, reducing the service life and performance of reinforced concrete structures. Hence, effective planning and maintenance strategies are paramount. However, the process of gathering and inferring information in traditional approaches is often characterized by uncertainty, inaccuracy, and ambiguity, further complicated by the intricate behavior and environmental conditions of the structures. Consequently, decision-making becomes challenging. To make informed decisions, accurate diagnosis of the type and extent of deterioration in reinforced concrete structures is essential. Accurate evaluation of deterioration involves a comprehensive investigation of the structure to identify the symptoms associated with each type of deterioration. These symptoms are derived from inspections, which are often uncertain, inaccurate, and ambiguous. To address these challenges, a fuzzy expert system is developed to diagnose the types and symptoms of deteriorations based on inspection data. This system leverages information obtained from visual inspections and advanced methods, including non-destructive and semi-destructive tests. By integrating this data into the fuzzy expert system, a more reliable and accurate diagnosis can be achieved. The development of a robust management system relies on obtaining and inferring information accurately.