Fuzzy-Tug of war structural active control for a seismically excited benchmark highway bridge

Document Type : Original Article

Authors

1 structural department, Babol Noshirvani university of technology

2 Associate Professor, Faculty of Civil Engineering, Noshirvani University of ‎Technology, Babol, Iran

3 Associate Professor, Faculty of Civil Engineering, Babol Noshirvani University of Technology, Babol, Iran

4 Associate Professor, Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

In this paper a new optimized active control algorithm based on combination of fuzzy neural network and a new highly efficient meta-heuristic population based optimization method extracted from Tug of War competition presents under different earthquake loads. The Efficiency of the proposed control method has been evaluated on the recently proposed nonlinear highway bridge benchmark, consist of nonlinear isolation bearings and nonlinear structural elements which equipped with the hydraulic actuators. A 5-layer neural network is used to obtain the control force. The neural network is utilized to approximate nonlinear rules of control. It gets instructions to the actuators installed between the deck and abutments. Stability of control laws to choose the parameters of the neural network are derived based on Lyapunov theory. The Results are presented in terms of a well-defined set of performance indices which is comparable to previous methods. The results show that the proposed controller method in spite of a simple description of the nonlinearities and non-detailed structural information can effectively reduce the responses of the bridge especially maximum of base shear, maximum of midspan displacement and maximum of acceleration. Also sensible decrease in responses such as maximum of ductility, dissipated energy and plasticity connections show that the proposed method is very effective in reducing structural damages.

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References

[1] Chen, Z., Han, Z., Fang, H. and Wei, K. (2018). Seismic vibration control for bridges with high-piers in Sichuan-Tibet Railway. Structural Engineering and Mechanics, 66(6), 749-759.
[2] Heo, G., Kim, C., Jeon, S., Lee, C. and Jeon, J., (2017). A hybrid seismic response control to improve performance of a two-span bridge. Structural Engineering and Mechanics, 61(5), 675-684.
[3] Spencer, Jr. B.F., Dyke, S.J and Deoskar, H.S. (1998). Benchmark problems in structural control—Part I: active mass driver system. Earthquake Engineering and Structural Dynamics, 27(11), 1127–1139.
[4] Spencer, Jr. B.F., Dyke, S.J and Deoskar, H.S. (1998). Benchmark problems in structural control—Part II: active tendon system. Earthquake Engineering and Structural Dynamics, 27(11), 1141–1147.
[5] Ohtori, Y., Christenson R.E., Spencer, B.F. and Dyke, S.J. (2004). Benchmark control problems for seismically excited nonlinear buildings. Engineering Mechanics, 130(4), 366–385.
[6] Yang, J.N., Agrawal, A.K., Samali, B. and Wu, J, C. (2004). A benchmark problem for response control of wind excited tall buildings. Engineering Mechanics, 130(4), 437–446.
[7] Dyke, S.J, Caicedo, J.M., Turan, G., Bergman, L.A. and Hague, S. (2003). Phase 1: benchmark control problem for seismic response of cable-stayed bridges. Structural Engineering, 129(7), 857–872.
[8] Caicedo, J.M., Dyke, S.J., Moon, S.J., Bergman, L.A., Turan, G., Hague, S. (2003). Phase II benchmark control problem for seismic response of cable-stayed bridges. Structural Control, 10(4), 137–168.
[9] Narasimhan, S., Nagarajaiah, S., Gavin, H. and Johnson, E.A. (2006). Smart base-isolated benchmark building. Part I: problem definition. Structural Control and Health Monitoring, 13(3), 573–588.
[10] Narasimhan, S. (2009). Robust direct adaptive controller for the nonlinear highway bridge benchmark. Structural Control and Health Monitoring, 138(4), 327-337.
[11] Saha, A., Saha, P. and Patro, S.K. (2017). Polynomial friction pendulum isolators (PFPIs) for seismic performance control of benchmark highway bridge. Earthquake Engineering and Engineering Vibration, 16(4), 827-840.
[12] Madhekar, S. N. and Jangid, R.S. (2011). Seismic Performance of Benchmark Highway Bridge Installed with Piezoelectric Friction Dampers. IES Journal Part A: Civil & Structural Engineering, 4(4), 191-212.
[13] Nagarajaiah, S. and Narasimhan, S. (2006). Base isolated benchmark building part ii: Phase i sample controllers for linear isolation systems. Structural Control and Health Monitoring, 13(2), 589–604.
[14] Narasimhan, S., Nagarajaiah, S. and Johnson, E.A. (2008). Structural control benchmark problem: Phase ii—nonlinear smart base isolated building subjected to near-fault earthquakes. Structural Control and Health Monitoring, 15(5), 653–656.
[15] Narasimhan, S., Nagarajaiah, S. and Johnson, E.A. (2008). Smart base-isolated benchmark building part iv: Phase ii sample controllers for nonlinear isolation systems. Structural Control and Health Monitoring, 15(5), 657–672.
[16] Agrawal, A., Tan, P., Nagarajaiah, S. and Zhang, J. (2008). Benchmark structural control problem for a seismically excited highway bridge—part i: Phase i problem definition. Structural Control and Health Monitoring, 16(5), 509-529.
[17] Tan, P. and Agrawal, A. (2008). Benchmark structural control problem for a seismically excited highway bridge—part ii: Phase i sample control design. Structural Control and Health Monitoring, 16(5), 530-548.
[18] Nagarajaiah, S., Narasimhan, S., Agrawal, A. and Tan, P. (2008). Benchmark structural control problem for a seismically excited highway bridge—part iii: Phase ii sample controller for the fully base-isolated case. Structural Control and Health Monitoring, 16(5), 549-563.
[19] Pang, H., Liu, F. and Xu, Z. (2018). Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization. neurocomputing, 306(10), 130-140.
[20] Khodabandolehlou, H., Pekcan, G., Fadali, M.S. and  Salem, M.M.A. (2017). Active neural predictive control of seismically isolated structures. Structural Control and Health Monitoring, 25(1), 184-195.
[21] Saha, A., Saha, P. and Patro, S.K. (2017). Polynomial friction pendulum isolators (PFPIs) for seismic performance control of benchmark highway bridge. Earthquake Engineering and Engineering Vibration, 16(4), 827-840.
[22] Park, K.S., Ok, S.Y. (2014). Fuzzy gain-tuning approach for active control system adaptable to physical constraints. KSCE journal of Civil Engineering, 19(5), 1468-1474. 
 [23] Kaveh, A., Zolghadr, A. (2016). A novel meta-heuristic algorithm: tug of war optimization. International Journal of Optimization in Civil Engineering, 6(4), 469-492.
[24] Agrawal, A., Tan, P., Nagarajaiah, S. and Zhang, J. (2006). Benchmark structural control problem for a seismically excited highway bridge. In Proceedings of the 2006 Structures Congress, Brad Cross JF (ed.), SEI, St. Louis, MO.
[25] Mavroeidis, G.P., Dong, G. and Papageorgiou, A.S. (2004). Near-fault ground motions, and the response of elastic and inelastic single-degree-of-freedom (SDOF) systems. Earthquake Engineering and Structural Dynamics, 33(3), 1023-1049.
[26] Jangid, R.S. and Kelly, J.M. (2001). Base isolation for near-fault motions. Earthquake Engineering and Structural Dynamics, 30(5), 691-707.
[27] Ali, S.F. (2008). Semi-active Control of Earthquake Induced Vibrations in Structures using MR Dampers: Algorithm  Development, Experimental Verification and Benchmark Applications. Ph. D. Thesis. Indian Institute of Science.
[28] Ning, X.L., Tan, P., Huang, D.Y. and Zhou, F.L. (2009). Application of adaptive fuzzy sliding mode control to a seismically excited highway bridge. Structural Control and Health Monitoring, 16(6), 639-656.

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History

  • Receive Date: 01 October 2018
  • Revise Date: 14 January 2019
  • Accept Date: 18 January 2019

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