دوره 12، شماره 1 - ( 10-1400 )                   جلد 12 شماره 1 صفحات 67-47 | برگشت به فهرست نسخه ها

XML English Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Payandeh-Sani M, Ahmadi-Nedushan B. SEMI-ACTIVE NEURO-CONTROL FOR MINIMIZING SEISMIC RESPONSE OF BENCHMARK STRUCTURES. International Journal of Optimization in Civil Engineering 2022; 12 (1) :47-67
URL: http://ijoce.iust.ac.ir/article-1-504-fa.html
SEMI-ACTIVE NEURO-CONTROL FOR MINIMIZING SEISMIC RESPONSE OF BENCHMARK STRUCTURES. عنوان نشریه. 1400; 12 (1) :47-67

URL: http://ijoce.iust.ac.ir/article-1-504-fa.html


چکیده:   (5397 مشاهده)
This article presents numerical studies on semi-active seismic response control of structures equipped with Magneto-Rheological (MR) dampers. A multi-layer artificial neural network (ANN) was employed to mitigate the influence of time delay, This ANN was trained using data from the El-Centro earthquake. The inputs of ANN are the seismic responses of the structure in the current step, and the outputs are the MR damper voltages in the current step. The required training data for the neural controller is generated using genetic algorithm (GA). Using the El-Centro earthquake data, GA calculates the optimal damper force at each time step. The optimal voltage is obtained using the inverse model of the Bouc-Wen based on the predicted force and the corresponding velocity of the MR damper. This data is stored and used to train a multi-layer perceptron neural network. The ANN is then employed as a controller in the structure. To evaluate the efficiency of the proposed method, three- story, seven- story and twenty-story structures with a different number of MR dampers were subjected to the Kobe, Northridge, and Hachinohe earthquakes. The maximum reduction in structural drifts in the three-story structure are 13.05%, 39.90%, 15.89%, and 8.21%, for the El-Centro, Hachinohe, Kobe, and Northridge earthquakes, respectively. As the control structure is using a pre-trained neural network, the computation load in the event of an earthquake is extremely low. Additionally, as the ANN is trained on seismic pre-step data to predict the damper's current voltage, the influence of time lag is also minimized.
متن کامل [PDF 670 kb]   (2244 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: 1400/10/28 | پذیرش: 1400/10/30 | انتشار: 1400/10/30

ارسال نظر درباره این مقاله : نام کاربری یا پست الکترونیک شما:
CAPTCHA

بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.

کلیه حقوق این وب سایت متعلق به دانشگاه علم و صنعت ایران می باشد.

طراحی و برنامه نویسی : یکتاوب افزار شرق

© 2024 CC BY-NC 4.0 | Iran University of Science & Technology

Designed & Developed by : Yektaweb