چكيده به لاتين
Dams are one of the most important concrete structures that are used to control surface water in any country with different purposes such as drinking water, electricity generation, agricultural water supply, and other items. In recent years, Artificial neural networks, efficiency, and high value to speed up computational processes in a wide of issues engineering topics branches at various. also, The use of self-organizing neural networks to provide solutions to those problems for which there is no solution or whose solution is not easily accessible has shown significant results. In this research, in order to evaluate the reliability of concrete dams, the fuzzy gray theoretical structure and self-organizing neural network are used. In addition, using this approach, structural loads are obtained from nonlinear analysis based on the elastoplastic behavior of structural materials, which is a kind of a new limit state. Also, considering that the proposed method is studied for the first time in hydraulic structures, it is, therefore, an innovative aspect to evaluate the reliability of these structures. Among the evaluated approaches, the most common problem is the use of basic neural networks to evaluate the available data. The use of neural networks may increase the volume of data and processing. Given that the issue of reliability is a time-based challenge, reducing execution time and evaluation can be very effective in the efficiency of analysis. In general, a method for estimating the level of reliability in concrete dams is considered, considering the randomness and grayness of the parameters and the ambiguity of the failure criteria. In this study, first, due to the fact that the volume of processing data is very high, the available information is managed using a self-organizing neural network structure. Due to the fact that in the selforganizing neural network, the size of the data is significantly reduced,the introduction of a new method of assessing reliability based on this approach, while maintaining efficiency and increasing the cost of computing can be reduced. Then, in order to quantify the gray properties of the random variables, continuous bootstrap sampling is applied in the gray system theory. To compensate for the inability to describe the criteria of fuzzy failure, the structure of the vector sample based on the model, will be "optimized".
