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Smart Control of Seismically Excited Highway Bridges

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Computational Methods in Earthquake Engineering

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 44))

Abstract

This chapter proposes a novel smart fuzzy control algorithm for mitigation of dynamic responses of seismically excited bridge structures equipped with control devices. The smart fuzzy controller is developed through the combination of discrete wavelet transform, backpropagation neural networks, and Takagi-Sugeno fuzzy model. To demonstrate the effectiveness of the proposed smart fuzzy controller, it is tested on a highway bridge equipped with magneto rheological (MR) dampers. It controls the smart dampers installed on the abutments of the highway bridge structure. The 1940 El-Centro and Kobe earthquakes are used as disturbance signals. It is demonstrated that the smart fuzzy controller is effective in reducing the structural responses of the highway bridge under a variety of seismic excitations.

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References

  1. Agrawal A, Tan P, Nagarajaiah S, Zhang J (2009) Benchmark structural control problem for a seismically excited highway bridge—Part I: Phase I problem definition. Struct Control Health Monit 16:509–529

    Article  Google Scholar 

  2. Ahlawat AS, Ramaswamy A (2002) Multi objective optimal design of FLC driven hybrid mass damper for seismically excited structures. Earthquake Eng Struct Dynam 31:1459–1479

    Article  Google Scholar 

  3. Arsava SK, Kim Y, El-Korchi T, Park HS (2013) Nonlinear system identification of smart structures under high impact loads. J Smart Mater Struc 22. doi:10.1088/0964-1726/22/5/055008

  4. Arsava SK, Chong JW, Kim Y (2014) A novel health monitoring scheme for smart structures. J Vib Control. doi:10.1177/1077546314533716

    Google Scholar 

  5. Arsava SK, Nam Y, Kim Y (2015) Nonlinear system identification of smart reinforced concrete structures under high impact loads. J Vib Control. doi:10.1177/1077546314563966

    Google Scholar 

  6. Arsava SK, Kim Y, Kim KH, Shin BS (2015) Smart fuzzy control of reinforced concrete structures excited by collision-type forces. Expert Syst Appl 42(21):7929–7941

    Article  Google Scholar 

  7. Cha YJ, Agrawal AK, Kim Y, Raich A (2012) Multi-objective genetic algorithms for cost-effective distributions of actuators and sensors in large structures. Expert Syst Appl 39:7822–7833

    Article  Google Scholar 

  8. Cha YJ, Kim Y, Raich A, Agrawal AK (2013) Multi-objective optimization for actuator and sensor layouts of actively controlled 3D buildings. J Vib Control 19:942–960

    Article  Google Scholar 

  9. Chen Y, Yang B, Abraham A, Peng L (2007) Automatic design of hierarchical Takagi-Sugeno Type fuzzy systems using evolutionary algorithms. IEEE Trans Fuzzy Syst 15:385–397

    Article  Google Scholar 

  10. Chong JW, Kim Y, Chon K (2014) Nonlinear multiclass support vector machine-based health monitoring system for buildings employing magnetorheological dampers. J Intell Mater Syst Struct 25:1456–1468

    Article  Google Scholar 

  11. Du H, Zhang N (2008) Application of evolving Takagi-Sugeno Fuzzy Model to nonlinear system identification. Appl Soft Comput 8:676–686

    Article  Google Scholar 

  12. Faravelli L, Yao T (1996) Use of adaptive networks in fuzzy control of civil structures. Microcomput Civil Eng 12:67–76

    Article  Google Scholar 

  13. Gu Z, Oyadiji S (2008) Application of MR damper in structural control using ANFIS Method. Comput Struct 86:427–436

    Article  Google Scholar 

  14. Gurley K, Kareem A (1999) Applications of wavelet transforms in earthquake, wind and ocean engineering. Eng Struct 21:149–167

    Article  Google Scholar 

  15. Hughes JE, Kim Y, El-Korchi T (2015) Radar technology for structural hazard mitigation. J Vib Control

    Google Scholar 

  16. Hung SL, Huang CS, Wen CM, Hsu YC (2003) Nonparametric identification of a building structure from experimental data using wavelet neural network. Computer-Aided Civil Infrastruct Eng 18:356–368

    Article  Google Scholar 

  17. Hurlebaus S, Gaul L (2006) Smart structure dynamics. Mech Syst Signal Process 20:255–286

    Article  Google Scholar 

  18. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  19. Johansen TA (1994) Fuzzy model based control: stability, robustness, and performance issues. IEEE Trans Fuzzy Syst 2:221–234

    Article  Google Scholar 

  20. Johansen TA, Babuška R (2003) Multiobjective identification of Takagi-Sugeno fuzzy models. IEEE Trans Fuzzy Syst 11:847–860

    Article  Google Scholar 

  21. Kim Y, Langari R (2007) Nonlinear identification and control of a building structure with a magnetorheological damper system. American control conference, New York

    Google Scholar 

  22. Kim Y, Hurlebaus S, Sharifi R, Langari R (2009a) Nonlinear identification of MIMO smart structures. ASME dynamic systems and control conference, Hollywood, California

    Google Scholar 

  23. Kim Y, Langari R, Hurlebaus S (2009) Semiactive nonlinear control of a building using a magnetorheological damper system. Mech Syst Signal Process 23:300–315

    Article  Google Scholar 

  24. Kim Y, Hurlebaus S, Langari R (2010) Control of a seismically excited benchmark building using linear matrix inequality-based semiactive nonlinear fuzzy control. ASCE J Struct Eng 136(8):1023–1026

    Article  Google Scholar 

  25. Kim Y, Langari R, Hurlebaus S (2010) Model-based multi-input, multi-output supervisory semiactive nonlinear fuzzy controller. Computer-Aided Civil Infrastruct Eng 25:387–393

    Article  Google Scholar 

  26. Kim Y, Kim C, Langari R (2010) Novel bio-inspired smart control for hazard mitigation of civil structures. J Smart Mater Struct 19:115009. doi:10.1088/0964-1726/19/11/115009

    Article  Google Scholar 

  27. Kim Y, Hurlebaus S, Langari R (2011) Fuzzy identification of building-MR damper system. Int J Intell Fuzzy Syst 22(4):185–205

    MathSciNet  MATH  Google Scholar 

  28. Kim Y, Chong JW, Chon K, Kim JM (2013) Wavelet-based AR-SVM for health monitoring of smart structures. J Smart Mater Struct 22(1):015003. doi:10.1088/0964-1726/22/1/015003

    Article  Google Scholar 

  29. Kim Y, Mallick R, Bhowmick S, Chen B (2013) Nonlinear system identification of large-scale smart pavement systems. Expert Syst Appl 40:3551–3560

    Article  Google Scholar 

  30. Kim Y, Kim KH, Shin BS (2014) Fuzzy model forecasting of offshore bar-shape profiles under high waves. Expert Syst Appl 41:5771–5779

    Article  MathSciNet  Google Scholar 

  31. Kim Y, Bai JW, Albano LD (2014) Fragility estimates of smart structures with sensor faults. J Smart Mater Struct 22:125012. doi:10.1088/0964-1726/22/12/125012

    Article  Google Scholar 

  32. Kim Y, Shin SS, Plummer JD (2014) A wavelet-based autoregressive fuzzy model for forecasting algal blooms. Environ Model Softw 62:1–10

    Article  Google Scholar 

  33. Kim Y, Kim YH, Lee S (2015) Multivariable nonlinear identification of smart buildings. Mech Syst Signal Process 62–63:254–271

    Article  Google Scholar 

  34. Langari R (1999) Past, present and future of fuzzy control: a case for application of fuzzy logic in hierarchical control. In: Proceedings, 18th international conference of the north american fuzzy information processing society-NAFIPS, New York City, New York, USA, pp 760–765

    Google Scholar 

  35. Mohammadzadeh S, Kim Y, Ahn J (2015) PCA-based neuro-fuzzy model for system identification of smart structures. J Smart Struct Syst 15(4):1139–1158

    Article  Google Scholar 

  36. Mitchell R, Kim Y, El-Korchi T (2012) System identification of smart structures using a wavelet neuro-fuzzy model. J Smart Mater Struct 21. doi:10.1088/0964-1726/21/11/115009

  37. Mitchell R, Kim Y, El-Korchi T, Cha YJ (2013) Wavelet-neuro-fuzzy control of hybrid building-active tuned mass damper system under seismic excitations. J Vib Control 19(12):1881–1894

    Article  Google Scholar 

  38. Mitchell R, Cha YJ, Kim Y, Mahajan A (2015) Active control of highway bridges subject to a variety of earthquake loads. Earthq Eng Eng Vib 14(2):253–263

    Article  Google Scholar 

  39. Sharifi R, Kim Y, Langari R (2010) Sensor fault isolation and detection of smart structures J Smart Mater Struct 19. doi:10.1088/0964-1726/19/10/105001

  40. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  41. Yager RR, Filev DP (1993) Unified Structure and parameter identification of fuzzy models. IEEE Trans Syst Man Cybern 23:1198–1205

    Article  Google Scholar 

  42. Yan G, Zhou LL (2006) Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers. J Sound Vib 296:368–382

    Article  Google Scholar 

  43. Yang YN, Lin S (2005) Identification of parametric variations of structures based on least squares estimation and adaptive tracking technique. ASCE J Eng Mech 131:290–298

    Article  Google Scholar 

  44. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Civil Engineering, California Baptist University, Riverside, CA, USA

    Yeesock Kim

  2. Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA, USA

    Aniket Anil Mahajan

Authors
  1. Yeesock Kim
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  2. Aniket Anil Mahajan
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Corresponding author

Correspondence to Yeesock Kim .

Editor information

Editors and Affiliations

  1. Civil Engineering, National Technical University of Athens, Athens, Greece

    Manolis Papadrakakis

  2. Department of Civil Engineering and Energy Technology, Oslo and Akershus University College of Applied Sciences, Oslo, Norway

    Vagelis Plevris

  3. School of Civil Engineering, National Technical University of Athens School of Civil Engineering, Athens, Greece

    Nikos D. Lagaros

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Kim, Y., Mahajan, A.A. (2017). Smart Control of Seismically Excited Highway Bridges. In: Papadrakakis, M., Plevris, V., Lagaros, N. (eds) Computational Methods in Earthquake Engineering. Computational Methods in Applied Sciences, vol 44. Springer, Cham. https://doi.org/10.1007/978-3-319-47798-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-47798-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47796-1

  • Online ISBN: 978-3-319-47798-5

  • eBook Packages: EngineeringEngineering (R0)

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