پارامترهای بهینه میراگر جرمی تنظیم شده برای سازه‌های بلند به کمک شبکه‌های‌عصبی مصنوعی

There are a variety of tools like vibration absorbers to reduce the vibration of structures by dissipating imposed energy. Tuned mass Damper is a vibration system with a mass and spring, that usually installed on the top of a structure. Tuned mass dampers are kind of absorbers that could reduce vibrations if the parameters of frequency and damping are well tuned. There are many analytical and empirical relations to identify these parameters obtained by structure simplification and loading. The paper demonstrates that neural networks can be used effectively for the identification optimal parameters of tuned mass dampers.In this paper, a new method is proposed to determine these parameters. For this purpose, several buildings with a number of different stories (8 to 80) are first created in MATLAB code. After obtaining the stiffness, damping and mass matrix of the structures, the program enters the Simulink environment and then structure with TMD tuned in the approximate range of optimal parameters is analyzed under different earthquakes. These ground motion records, including two near field and two far field records that suggested by the International Association of Structural control are used. To increase the number of data used to train artificial neural network and reduce uncertainty, in addition to the records mentioned, four other earthquake was considered. The TMD Mass, damping coefficient and frequencies is assumed as the design variables of the controller; and the objective is set as the reduction of the maximum displacement of the building. In the end, the parameters for the maximum reduction in lateral displacement of structures are identified. Then neural network training begins to have a complete database of structures with different stories. The number of hidden layer neural network are ten, and the number of layer's output are two considered. To train the neural network of Six hundred and forty-four date has been used. Of these numbers, the Seventy percents are share of training data, and fifteen percents are share of Validation data, and fifteen percents are share of test data. Accordingly, the neural network after training can evaluate frequency and damping ratio of TMD based on input such as frequency and TMD mass ratio. Using this method, it has been observed that errors in the frequency and damping optimum TMD have been reduced particularly compared to empirical relations of Den Hartog. This reduces the errors caused by factors not considered in relation to the various reasons. For example, these factors maybe not yet identify, or because of the complexity of behavior have been simplified. However, using a neural network without fully understanding the parameters influencing the behavior of TMD exists, these factors indirectly to predict the behavior of TMD considered. So the results of this method can be used to optimize the parameters of TMD, to be used with greater confidence. Finally, these values are compared with those provided by Den Hartog in a high-rise building. The results showed that for the building, parameters provided by neural network approximately 1 percent of the actual value of the optimal values is different.