Optimal Design of MR Dampers Using NSGA-II Algorithm

Document Type : Regular Article

Authors

1 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

2 Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

Abstract

In recent years, new ideas and solutions have been proposed by scientists and researchers to control the response of structures against seismic excitations. In this article, The Optimization of semi-active control systems using MR dampers has been studied to reduce the structure's response under earthquake forces. For this purpose, three frames of five, eight, and eleven stories have been examined as numerical examples. A multi-objective optimization approach based on the NSGA-II algorithm is used to control the response of structures and the fuzzy logic algorithm is used to determine the force of these dampers. The values of maximum displacement, acceleration, and inter-story drift of the top floor have been selected as objective functions. The position of dampers has been optimized to obtain optimal practical solutions. The results show that the responses are significantly reduced when using a semi-active MR damper and the arrangement of the dampers has a great impact on the amount of this reduction.

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Main Subjects


[1]     Babaei M, Moniri A. Use of Tuned Mass Dampers in Controlling the Vibrations of Steel Structures with Vertical Irregularity of Mass. Comput Eng Phys Model 2018;1:83–94. https://doi.org/10.22115/cepm.2018.137303.1035.
[2]     Vishwakarma PN, Mishra P, Sharma SK. Characterization of a magnetorheological fluid damper a review. Mater Today Proc 2022;56:2988–94. https://doi.org/10.1016/j.matpr.2021.11.143.
[3]     Al-Fahdawi OA., Barroso LR, Soares RW. Adaptive Neuro-Fuzzy and Simple Adaptive Control Methods for Alleviating the Seismic Responses of Coupled Buildings with Semi-active Devices: Comparative Study. Soft Comput Civ Eng 2019;3:1–20. https://doi.org/10.22115/SCCE.2019.199731.1128.
[4]     Ferdaus MM, Rashid MM, Hasan MH, Rahman MA. Optimal design of Magneto-Rheological damper comparing different configurations by finite element analysis. J Mech Sci Technol 2014;28:3667–77. https://doi.org/10.1007/s12206-014-0828-5.
[5]     Nagarajaiah S, Sahasrabudhe S. Seismic response control of smart sliding isolated buildings using variable stiffness systems: an experimental and numerical study. Earthq Eng Struct Dyn 2006;35:177–97. https://doi.org/10.1002/eqe.514.
[6]     Gutierrez Soto M, Adeli H. Placement of control devices for passive, semi-active, and active vibration control of structures. Sci Iran 2013;20:1567–78.
[7]     Hashemi MR, Vahdani R, Gerami M, Kheyroddin A. A New Approach to the Optimal Placement of the Viscous Damper Based on the Static Force Distribution Pattern. Period Polytech Civ Eng 2022;66:866–75. https://doi.org/10.3311/PPci.17238.
[8]     Wani ZR, Tantray M. Study on integrated response-based adaptive strategies for control and placement optimization of multiple magneto-rheological dampers-controlled structure under seismic excitations. J Vib Control 2022;28:1712–26. https://doi.org/10.1177/10775463211000483.
[9]     Dyke SJ, Spencer Jr. BF, Sain MK, Carlson JD. Experimental Verification of Semi-Active Structural Control Strategies using Acceleration Feedback. Proc. 3rd Int. Conf. Motion Vib. Control, vol. 3, 1996, p. 291–6.
[10]   Qu WL, Xu YL. Semi-active control of seismic response of tall buildings with podium structure using ER/MR dampers. Struct Des Tall Build 2001;10:179–92. https://doi.org/10.1002/tal.177.
[11]   Zhou L, Chang C-C, Wang L-X. Adaptive Fuzzy Control for Nonlinear Building-Magnetorheological Damper System. J Struct Eng 2003;129:905–13. https://doi.org/10.1061/(asce)0733-9445(2003)129:7(905).
[12]   Renzi E, Serino G. Testing and modelling a semi-actively controlled steel frame structure equipped with MR dampers. Struct Control Heal Monit 2004;11:189–221. https://doi.org/10.1002/stc.36.
[13]   Yan G, Zhou LL. Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers. J Sound Vib 2006;296:368–82. https://doi.org/10.1016/j.jsv.2006.03.011.
[14]   Amini F, Ghaderi P. Optimal locations for MR dampers in civil structures using improved Ant Colony algorithm. Optim Control Appl Methods 2012;33:232–48. https://doi.org/10.1002/oca.991.
[15]   Askari M, Li J, Samali B. Cost-effective multi-objective optimal positioning of magnetorheological dampers and active actuators in large nonlinear structures. J Intell Mater Syst Struct 2017;28:230–53. https://doi.org/10.1177/1045389X16649449.
[16]   Lopes MA, Soeiro FJCP, Santos da Silva JG. Structural optimization of concrete volume for machine foundation using genetic algorithms. J Soft Comput Civ Eng 2019;3:62–81. https://doi.org/10.22115/SCCE.2019.203066.1129.
[17]   Sanaei E, Babaei M. Cellular Automata in Topology Optimization of Continuum Structures. Int J Eng Sci Technol 2011;3. https://doi.org/10.4314/ijest.v3i4.68539.
[18]   Hasançebi O, Çarbaş S, Doğan E, Erdal F, Saka MP. Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput Struct 2009;87:284–302. https://doi.org/10.1016/j.compstruc.2009.01.002.
[19]   Rajeev S, Krishnamoorthy CS. Discrete optimization of structures using genetic algorithms. J Struct Eng 1992;118:1233–50.
[20]   Akbari M, Henteh M. Comparison of Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO) for Discrete and Continuous Size Optimization of 2D Truss Structures. J Soft Comput Civ Eng 2019;3:76–97. https://doi.org/10.22115/SCCE.2019.195713.1117.
[21]   Fathali MA, Hoseini Vaez SR. Optimum performance-based design of eccentrically braced frames. Eng Struct 2020;202:109857. https://doi.org/10.1016/j.engstruct.2019.109857.
[22]   Abedini H, Hoseini Vaez SR, Zarrineghbal A. Optimum design of buckling-restrained braced frames. Structures 2020;25:99–112. https://doi.org/10.1016/j.istruc.2020.03.004.
[23]   Babaei M, Mollayi M. An improved constrained differential evolution for optimal design of steel frames with discrete variables. Mech Based Des Struct Mach 2020;48:697–723. https://doi.org/10.1080/15397734.2019.1657890.
[24]   Seraji N, Babaei M. Discrete sizing optimization of steel structures using modified fireworks algorithm n.d.
[25]   Sanaei E, Babaei M. Topology optimization of structures using cellular automata with constant strain triangles. Int J Civ Eng 2012;10:179–88.
[26]   Camp C V., Bichon BJ, Stovall SP. Design of Steel Frames Using Ant Colony Optimization. J Struct Eng 2005;131:369–79. https://doi.org/10.1061/(ASCE)0733-9445(2005)131:3(369).
[27]   Kaveh A, Talatahari S. An improved ant colony optimization for the design of planar steel frames. Eng Struct 2010;32:864–73. https://doi.org/10.1016/j.engstruct.2009.12.012.
[28]   Talatahari S, Nouri M, Tadbiri F, Branch S, Azad I. OPTIMIZATION OF SKELETAL STRUCTURAL USING. Int J Optim Civ Eng 2012;2:557–71.
[29]   Farshchin M, Maniat M, Camp C V., Pezeshk S. School based optimization algorithm for design of steel frames. Eng Struct 2018;171:326–35. https://doi.org/10.1016/j.engstruct.2018.05.085.
[30]   Pezeshk S, Camp C V., Chen D. Design of Nonlinear Framed Structures Using Genetic Optimization. J Struct Eng 2000;126:382–8. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:3(382).
[31]   Babaei M. Multi-objective optimal number and location for steel outrigger-belt truss system. J Eng Sci Technol 2017;12:2599–612.
[32]   Babaei M, Sanaei E. Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization. Front Struct Civ Eng 2016;10:472–80. https://doi.org/10.1007/s11709-016-0368-4.
[33]   Babaei M, Mollayi M. Multi-objective Optimization of Reinforced Concrete Frames Using NSGA-II Algorithm. Eng Struct Technol 2016;8:157–64. https://doi.org/10.3846/2029882X.2016.1250230.
[34]   Ghasemof A, Mirtaheri M, Karami Mohammadi R. Effects of demand parameters in the performance-based multi-objective optimum design of steel moment frame buildings. Soil Dyn Earthq Eng 2022;153:107075. https://doi.org/10.1016/j.soildyn.2021.107075.
[35]   Ghasemof A, Mirtaheri M, Karami Mohammadi R. Multi-objective optimization for probabilistic performance-based design of buildings using FEMA P-58 methodology. Eng Struct 2022;254:113856. https://doi.org/10.1016/j.engstruct.2022.113856.
[36]   Jung H-J, Spencer BF, Lee I-W. Control of Seismically Excited Cable-Stayed Bridge Employing Magnetorheological Fluid Dampers. J Struct Eng 2003;129:873–83. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:7(873).
[37]   Ok S-Y, Kim D-S, Park K-S, Koh H-M. Semi-active fuzzy control of cable-stayed bridges using magneto-rheological dampers. Eng Struct 2007;29:776–88. https://doi.org/10.1016/j.engstruct.2006.06.020.
  • Receive Date: 14 June 2022
  • Revise Date: 04 December 2022
  • Accept Date: 26 December 2022
  • First Publish Date: 26 December 2022