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Earthquake Engineering and Engineering Vibration

, Volume 17, Issue 4, pp 903–915 | Cite as

Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load

  • Meysam Ramezani
  • Akbar Bathaei
  • Amir K. Ghorbani-Tanha
Article
  • 61 Downloads

Abstract

High-rise buildings are usually considered as flexible structures with low inherent damping. Therefore, these kinds of buildings are susceptible to wind-induced vibration. Tuned Mass Damper (TMD) can be used as an effective device to mitigate excessive vibrations. In this study, Artificial Neural Networks is used to find optimal mechanical properties of TMD for high-rise buildings subjected to wind load. The patterns obtained from structural analysis of different multi degree of freedom (MDF) systems are used for training neural networks. In order to obtain these patterns, structural models of some systems with 10 to 80 degrees-of-freedoms are built in MATLAB/SIMULINK program. Finally, the optimal properties of TMD are determined based on the objective of maximum displacement response reduction. The Auto-Regressive model is used to simulate the wind load. In this way, the uncertainties related to wind loading can be taken into account in neural network’s outputs. After training the neural network, it becomes possible to set the frequency and TMD mass ratio as inputs and get the optimal TMD frequency and damping ratio as outputs. As a case study, a benchmark 76-story office building is considered and the presented procedure is used to obtain optimal characteristics of the TMD for the building.

Keywords

artificial neural networks tuned mass damper wind load auto-regressive model optimal frequency and damping 

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Copyright information

© Institute of Engineering Mechanics, China Earthquake Administration and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Meysam Ramezani
    • 1
  • Akbar Bathaei
    • 1
  • Amir K. Ghorbani-Tanha
    • 1
  1. 1.School of Civil Engineering, College of EngineeringUniversity of TehranTehranIran

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