Browse > Article
http://dx.doi.org/10.12989/sss.2019.23.1.001

Semi-active seismic control of a 9-story benchmark building using adaptive neural-fuzzy inference system and fuzzy cooperative coevolution  

Bozorgvar, Masoud (Department of Civil Engineering, Arak Branch, Islamic Azad University)
Zahrai, Seyed Mehdi (Center of Excellence for Engineering and Management of Civil Infrastructures, School of Civil Engineering, College of Engineering, The University of Tehran)
Publication Information
Smart Structures and Systems / v.23, no.1, 2019 , pp. 1-14 More about this Journal
Abstract
Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.
Keywords
semi-active seismic control; MR damper; Adaptive Neural-Fuzzy Inference System (ANFIS); Fuzzy Cooperative Coevolution (Fuzzy CoCo); Genetic Algorithm (GA);
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Ahlawat, A.S. and Ramaswamy, A. (2004a), "Multiobjective optimal fuzzy logic control system for response control of wind-excited tall buildings", J. Eng. Mech., 130(4), 524-530.   DOI
2 Ahlawat, A.S. and Ramaswamy, A. (2004b), "Multiobjective optimal fuzzy logic controller driven active and hybrid control systems for seismically excited nonlinear buildings", J. Eng. Mech., 130(4), 416-423.   DOI
3 Ali, S.F. (2010), Semi-active control of earthquake induced vibrations in structures using MR dampers: algorithm development, experimental verification and benchmark applications, (Doctoral dissertation, G22641).
4 Ali, S.F. and Ramaswamy, A. (2006), "Benchmark control problem for highway bridge based on FLC", Proceedings of the Structures Congress 2006: Structural Engineering and Public Safety.
5 Bathaei, A., Zahrai, S.M. and Ramezani, M. (2017), "Semi-active seismic control of an 11-DOF building model with TMD+ MR damper using type-1 and-2 fuzzy algorithms", J. Vib. Control, 1077546317696369.
6 Cord, O. (2001), Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases (Vol. 19),World Scientific.
7 Hashemi, S.M.A., Haji Kazemi, H. and Karamodin, A. (2016), "Localized genetically optimized wavelet neural network for semi-active control of buildings subjected to earthquake", Struct. Control Health Monit., 23(8), 1074-1087.   DOI
8 Fayezioghani, A. and Moharrami, H. (2015), "Optimal control via integrating the dynamics of magnetorheological dampers and structures", Civil Eng. Infrastruct. J., 48(2), 345-357.
9 Ghaboussi, J. and Joghataie, A. (1995), "Active control of structures using neural networks", J. Eng. Mech.- ASCE, 121(4), 555-567.   DOI
10 Gu, Z.Q. and Oyadiji, S.O. (2008), "Application of MR damper in structural control using ANFIS method", Comput. Struct., 86(3), 427-436.   DOI
11 Housner, G., Bergman, L.A., Caughey, T.K., Chassiakos, A.G., Claus, R.O., Masri, S.F., Skelton, R.E., Soong, T.T., Spencer, B.F. and Yao, J.T. (1997), "Structural control: past, present, and future", J. Eng. Mech., 123(9), 897-971.   DOI
12 Huang, Z.S., Wu, C. and Hsu, D.S. (2009), "Semi-active fuzzy control of mr damper on structures by genetic algorithm", J. Mech., 25(1), N1-N6.   DOI
13 Jang, J.S. (1993), "ANFIS: adaptive-network-based fuzzy inference system", IEEE T. Syst, Man Cy., 23(3), 665-685.   DOI
14 Karamodin A. (2007), Damage control of structures subjected to earthquake, Ph.D. Dissertation, Ferdowsi University of Mashhad, Iran.
15 Karamodin, A. and Haji Kazemi, H. (2008), "Semi-active control of structures using neuro-predictive algorithm for MR dampers", Struct. Control Health Monit., 278.
16 Kim, D.H., Seo, S.N. and Lee, I.W. (2004), "Optimal neurocontroller for nonlinear benchmark structure", J. Eng. Mech., 130(4), 424-429.   DOI
17 Potter, M.A. and De Jong, K.A. (2000), "Cooperative coevolution: An architecture for evolving coadapted subcomponents", Evolution. Comput., 8(1), 1-29.   DOI
18 Ohtori, Y., Christenson, R.E., Spencer Jr, B.F. and Dyke, S.J. (2004), "Benchmark control problems for seismically excited nonlinear buildings", J. Eng. Mech., 130(4), 366-385.   DOI
19 Pena-Reyes, C.A. and Sipper, M. (2001), "Fuzzy CoCo: A cooperative-coevolutionary approach to fuzzy modeling", IEEE T. Fuzzy Syst., 9(5), 727-737.   DOI
20 Potter, M.A. (1997), The design and analysis of a computational model of cooperative coevolution (Doctoral dissertation, George Mason University).
21 Ramezani, M. and Zahrai, S.M. (2016), "Optimal parameters of tuned mass damper for tall buildings by neural networks", Modares Civil Eng. J. (M.C.E.J), 16(4), 109-122.
22 Ramezani, M., Bathaei, A. and Zahrai, S.M. (2017), "Designing fuzzy systems for optimal parameters of TMDs to reduce seismic response of tall buildings", Smart Struct. Syst., 19(3), 269-277.   DOI
23 Reigles, D.G. and Symans, M.D. (2006), "Supervisory fuzzy control of a base-isolated benchmark building utilizing a neurofuzzy model of controllable fluid viscous dampers", Struct. Control Health Monit., 13(2-3), 724-747.   DOI
24 Ross, T.J. (2009), Fuzzy logic with engineering applications. John Wiley & Sons.
25 Spencer Jr, B.F. and Nagarajaiah, S. (2003), "State of the art of structural control", J. Struct. Eng., 129(7), 845-856.   DOI
26 Spencer Jr, B.F., Dyke, S.J., Sain, M.K. and Carlson, J. (1997), "Phenomenological model for magnetorheological dampers", J. Eng. Mech., 123(3), 230-238.   DOI
27 Zahrai, S.M., Zare, A., Khalili, M.K. and Asnafi, A. (2013), "Seismic design of fuzzy controller for semi-active tuned mass dampers using top stories as the mass", Asian J. Civil Eng. (BHRC), 14(3), 383-396.
28 The Math Works Inc. MATLAB 7.10.0, Natick, MA, 2010.
29 Uz, M.E. and Hadi, M.N. (2014), "Optimal design of semi active control for adjacent buildings connected by MR damper based on integrated fuzzy logic and multi-objective genetic algorithm", Eng. Struct., 69, 135-148.   DOI
30 Williams, M.S. and Sexsmith, R.G. (1995), "Seismic damage indices for concrete structures: a state-of-the-art review", Earthq. Spectra, 11(2), 319-349.   DOI