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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.

Polymer concrete, which contains silica fume powder and vinyl ester resin as two replacements for Portland cement, has improved mechanical properties and durability compared to ordinary concrete. Thus, this kind of concrete is considered to be a high-strength concrete that is resistant to corrosion and chemical attacks. In this paper, the effects of the combination of silica fume powder and vinyl ester resin as two Portland cement replacements on the workability and slump value, initial and final water absorption, compressive and tensile strength, and failure and fracture paths of the polymer concrete have been investigated. All investigations have been based on 16 different polymer concrete mixture designs. The results indicate that the optimum percentages for a combination of silica fume and vinyl ester resin, which has the maximum compressive strength (34.26 MPa) and the maximum tensile strength (4.92 MPa), are a combination of 10% silica fume and 5% vinyl ester resin. To evaluate the durability of polymer concrete, the water absorption of all mixture designs has also been measured. Accordingly, the mixture design, which includes a combination of 15% vinyl ester resin and 5% silica fume, has a minimum initial and final water absorption equal to 0.62% and 1.95%, respectively.

In this paper, an empirical model is developed for predicting the chloride diffusion coefficient for silica fume, metakaolin, zeolite, and portland cement (PC) concretes under long-term exposure in the splash zone of Qeshm Island, Iran. All investigations are based on 12 concrete mixture designs exposed to seawater for a maximum period of 50 months. The empirical model is developed by applying regression analysis based on Ficks Second Diffusion Law on the experimental results and those are compared with previous studies in this area. These comparisons indicate that the predicted chloride diffusion coefficient level is within a ±25% error margin in the specimens.