DEVELOPING AN INNOVATIVE MODEL TO ESTIMATE THE SERVICE LIFE OF REINFORCED CONCRETE STRUCTURES IN THE PERSIAN GULF USING ARTIFICIAL NEURAL NETWORK

One of the main causes of deterioration of reinforced concrete structures exposed to marine environments is steel corrosion due to chloride penetration into concrete. Studies and research in the field of durability models must be based on the special environmental conditions such as temperature, temperature changes, humidity, wind, and locally consumed materials such as cement, additives, pozzolan, etc. On the other hand, using foreign models can be misleading and result in poor decisions due to incompatibility with our country’s environmental condition. Based on the significant role of environmental conditions, in the present study, modeling was conducted using data from research sites located in the Persian Gulf in order to improve of the accuracy of the model in estimating the service life of reinforced concrete structures in this region. According to local investigations and examination of structures constructed in different parts of the Persian Gulf, it seems that the effect of environmental conditions of various regions of the Persian Gulf on the durability of concrete is very different [1]. In order to assess concrete durability in these conditions and develop a comprehensive model, it is necessary to consider the results of research in various areas of the region, and results of tests in a particular area are not sufficient. Therefore, results obtained from several research sites in this region were collected and used in this study.