Skip to main content
SpringerLink
Log in

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

  • Published:
Earthquake Engineering and Engineering Vibration Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aly AM (2014), “Proposed Robust Tuned Mass Damper for Response Mitigation in Buildings Exposed to Multidirectional Wind,” The Structural Design of Tall and Special Buildings, 23(9): 664–691.

    Article  Google Scholar 

  • Bekdas G and Nigdeli SM (2011), “Estimating Optimum Parameters of Tuned Mass Dampers Using Harmony Search,” Engineering Structures, 33(9): 2716–2723.

    Article  Google Scholar 

  • Brock JE (1946), “A Note on the Damped Vibration Absorber,” Journal of Applied Mechanics, 13(4): A–284.

    Google Scholar 

  • Canadian Structural Design Manual (1971), Supplement No. 4 to the National Building Code of Canada, Associate Committee on the National Building Code and National Research Council of Canada, Ottawa.

  • Chung LL, Wu LY, Yang CSW, Lien KH, Lin MC and Huang HH (2013), “Optimal Design Formulas for Viscous Tuned Mass Dampers in Wind-Excited Structures,” Structural Control and Health Monitoring, 20(3): 320–336.

    Article  Google Scholar 

  • Clark AJ (1988), “Multiple Passive TMDs for Reducing Earthquake Induced Building Motion,” Proceedings of Ninth World Conference on Earthquake Engineering, Tokyo-Kyoto, Japan, 5: 779–784.

    Google Scholar 

  • Connor JJ (2002), Introduction to Structural Motion Control, Prentice Hall PTR.

    Google Scholar 

  • Den Hartog JP (1956), Mechanical Vibrations, 4nd ed. McGraw-Hill, New York.

    Google Scholar 

  • Desu NB, Deb SK and Dutta A (2006), “Coupled Tuned Mass Dampers for Control of Coupled Vibrations in Asymmetric Buildings,” Structural Control and Health Monitoring, 13(5): 897–916.

    Article  Google Scholar 

  • Dyrbye C and Hansen SO (1996), Wind Loads on Structures, John Wiley & Sons, New York.

    Google Scholar 

  • Facchini L (1996), “The Numerical Simulation of Gaussian Cross-Correlated Wind Velocity Fluctuations by Means of a Hybrid Model,” Journal of Wind Engineering and Industrial Aerodynamics, 64(2–3): 187–202.

    Article  Google Scholar 

  • Falcon KC, Stone BJ, Simcock WD and Andrew C (1967), “Optimization of Vibration Absorbers: A Graphical Method for Use on Idealized Systems with Restricted Damping,” Journal of Mechanical Engineering Science, 9(5): 374–381.

    Article  Google Scholar 

  • Farshidianfar A and Soheili S (2013), “Ant Colony Optimization of Tuned Mass Dampers for Earthquake Oscillations of High-Rise Structures Including Soil–Structure Interaction,” Soil Dynamics and Earthquake Engineering, 51: 14–22.

    Article  Google Scholar 

  • Flood I and Kartam N (1994), “Neural Networks in Civil Engineering I: Principles and Understanding,” Journal of Computing in Civil Engineering, 8(2): 131–148.

    Article  Google Scholar 

  • Frahm H (1909), “Device for Damping Vibrations of Bodies,” U.S. Patent No. 989,958.

    Google Scholar 

  • Fujino Y and Abe M (1993), “Design Formulas for Tuned Mass Dampers Based on a Perturbation Technique,” Earthquake Engineering & Structural Dynamics, 22(10): 833–854.

    Article  Google Scholar 

  • Hadi M and Arfiadi Y (1998), “Optimum Design of Absorber for MDOF Structures,” Journal of Structural Engineering, 124(11): 1272–1280.

    Article  Google Scholar 

  • Haykin S (1998), Neural Networks: A Comprehensive Foundation, Second Edition, Prentice Hall PTR Upper Saddle River, NJ, USA.

    Google Scholar 

  • Huang Z and Chalabi ZS (1995), “Use of Time-Series Analysis to Model and Forecast Wind Speed,” Journal of Wind Engineering and Industrial Aerodynamics, 56(2): 311–322.

    Article  Google Scholar 

  • Iannuzzi A and Spinelli P (1987), “Artificial Wind Generation and Structural Response,” Journal of Structural Engineering, ASCE, 113(10): 2382–2398.

    Article  Google Scholar 

  • Iemura H and Pradono MH (2003), Earthquake Engineering Handbook, Chen and Scawthorne, Chapter 19, CRC Press, New York.

    Google Scholar 

  • Ioi T and Ikeda K (1978), “On the Dynamic Vibration Damped Absorber of the Vibration System,” Bulletin of Japanese Society of Mechanical Engineering, 21(151): 64–71.

    Article  Google Scholar 

  • Iwatani Y (1982), “Simulation of Multidimensional Wind Fluctuations Having Any Arbitrary Power Spectra and Cross Spectra,” Journal of Wind Engineering and Industrial Aerodynamics, 16(6): 670–681.

    Google Scholar 

  • Kosko B (1992), Neural Networks and Fuzzy Systems, Prentice Hall, New Jersey.

    Google Scholar 

  • Leung AYT and Zhang H (2009), “Particle Swarm Optimization of Tuned Mass Dampers,” Engineering Structures, 31(3): 715–728.

    Article  Google Scholar 

  • Levenberg K (1944), “A Method for the Solution of Certain Problems in Least Squares,” Quarterly of Applied Mathematics, 2(2): 164–168.

    Article  Google Scholar 

  • Li YQ and Dong SL (2001), “Random Wind Load Simulation and Computer Program for Large-Span Spatial Structures,” Spatial Structures, 7(3): 3–11.

    Google Scholar 

  • Liu MY, Chiang WL, Hwang JH and Chu CR (2008), “Wind-Induced Vibration of High-Rise Building with Tuned Mass Damper Including Soil-Structure Interaction,” Journal of Wind Engineering and Industrial Aerodynamics, 96(6-7): 1092–1102.

    Article  Google Scholar 

  • Luft RW (1979), “Optimal Tuned Mass Dampers for Building,” Journal of the Structural Division, 105(12): 2766–2772.

    Google Scholar 

  • Marquardt DW (1963), “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” Journal of the Society for Industrial and Applied Mathematics, 11(2): 431–441.

    Article  Google Scholar 

  • McNamara RJ (1977), “Tuned Mass Dampers for Building,” Journal of the Structural Division, ASCE, 103(9): 1785–1798.

    Google Scholar 

  • Nigdeli SM and Bekdas G (2014), “Optimum Tuned Mass Damper Approaches for Adjacent Structures,” Earthquakes and Structures, 7(6): 1071–1091.

    Article  Google Scholar 

  • Nigdeli SM and Bekdas G (2015), “Teaching-Learning-Based Optimization for Estimating Tuned Mass Damper Parameters,” 3rd International Conference on Optimization Techniques in Engineering (OTENG ‘15), Rome, Italy.

    Google Scholar 

  • Poggim P, Muselli M, Notton G, Cristofarim C and Louche A (2003), “Forecasting and Simulating Wind Speed in Corsica by Using an Autoregressive Model,” Energy Conversion and Management, 44(20): 3177–3196.

    Article  Google Scholar 

  • Pourzeynali S, Lavasani HH and Modarayi AH (2007), “Active Control of High Rise Building Structures Using Fuzzy Logic and Genetic Algorithms,” Engineering Structures, 29(3): 346–357.

    Article  Google Scholar 

  • Riera JD and Davenport AG (1998), Wind Effects on Buildings and Structures: Proceedings of the Jubileum Conference on Wind Effects on Buildings and Structures, Porto Alegre, Brazil, 25–29 May, Taylor & Francis.

    Google Scholar 

  • Simiu E and Scanlan RH (1996), Wind Effects on Structures-Fundamentals and Applications to Design, 3rd Edition, John Wiley & Sons, New York.

    Google Scholar 

  • Singh MP, Singh S and Moreschi LM (2002), “Tuned Mass Dampers for Response Control of Torsional Buildings,” Earthquake Engineering & Structural Dynamics, 31(4): 749–769.

    Article  Google Scholar 

  • Soong TT, Reinhorn AM, Aizawa S and Higashino M (1994), “Recent Structural Applications of Active Control Technology,” Journal of Structural Control, 1(1–2): 5–21.

    Google Scholar 

  • Steinbuch R (2011), “Bionic Optimization of the Earthquake Resistance of High Buildings by Tuned Mass Dampers,” Journal of Bionic Engineering, 8(3): 335–344.

    Article  Google Scholar 

  • Thompson AG (1981), “Optimum Tuning and Damping of a Dynamic Vibration Absorber Applied to a Force Excited and Damped Primary System,” Journal of Sound and Vibration, 77(3): 403–415.

    Article  Google Scholar 

  • Warburton GB (1982), “Optimal Absorber Parameters for Various Combinations of Response and Excitation Parameters,” Earthquake Engineering & Structural Dynamics, 10(3): 381–401.

    Article  Google Scholar 

  • Wu JK (1994), Neural Networks and Simulation Methods, Marcel Dekker, New York.

    Google Scholar 

  • Yang JN, Wu JC, Samali B and Agrawal AK (2004), “A Benchmark Problem for Response Control of Wind-Excited Tall Buildings,” Journal of Engineering Mechanics, 130(4): 437–446.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Civil Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran

    Meysam Ramezani, Akbar Bathaei & Amir K. Ghorbani-Tanha

Authors
  1. Meysam Ramezani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akbar Bathaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amir K. Ghorbani-Tanha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir K. Ghorbani-Tanha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ramezani, M., Bathaei, A. & Ghorbani-Tanha, A.K. Application of artificial neural networks in optimal tuning of tuned mass dampers implemented in high-rise buildings subjected to wind load. Earthq. Eng. Eng. Vib. 17, 903–915 (2018). https://doi.org/10.1007/s11803-018-0483-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11803-018-0483-4

Keywords

Search

Navigation