Skip to main content

Seismic Fragility Analysis of Faulty Smart Structures

  • Chapter
  • First Online:
Computational Methods in Earthquake Engineering

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

  • 1064 Accesses

Abstract

In this chapter, seismic vulnerability of smart structures is assessed using fragility analysis framework. The fragility analysis framework is effective to evaluate the performance and the vulnerability of structures under a variety of earthquake loads. To demonstrate the effectiveness of the seismic fragility analysis framework, a three-story steel frame building employing the nonlinear smart damping system is selected as a case study structure. To investigate the impact of sensor failures, various sensor damage case scenarios are considered. The seismic capacity of the smart building is determined based on the typical structural performance levels used in the literature. The unknown parameters for the seismic demand models are estimated using a Bayesian updating algorithm. Finally, the fragility curves of the smart structures under a variety of sensor damage cases are compared. It is proved from the extensive simulations that the proposed seismic fragility analysis framework is very effective in estimating the control performance of smart structures with sensor faults.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

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

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

    Google Scholar 

  3. Arsava SK, Kim Y (2015) Modeling of magnetorheological dampers under various impact forces. Shock Vib 2015, Article ID 905186, 20 p. doi:10.1155/2015/905186

  4. 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 

  5. 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 

  6. Ankireddi S, Yang HTY (1999) Neural networks for sensor fault correction in structural control, ASCE. J Struct Eng 125:1056–1064

    Article  Google Scholar 

  7. ASCE/SEI (2011) Seismic rehabilitation of existing buildings, ASCE/SEI 41-11, Reston, VA, American Society of Civil Engineers

    Google Scholar 

  8. ASCE (2000) Prestandard and commentary for the seismic rehabilitation of buildings (FEMA 356), Washington D.C., Prepared by American Society of Civil Engineers for the Federal Emergency Management Agency

    Google Scholar 

  9. Bai J-W, Gardoni P, Hueste MBD (2011) Story-specific demand models and seismic fragility estimates for multi-story buildings. Struct Saf 33:96–107

    Article  Google Scholar 

  10. Barnawi W, Dyke SJ (2008) Fragility based analysis of a 20-story benchmark building with smart device implementation. In: Proceedings of the 11th aerospace division international conference on engineering, science, construction, and operations in challenging environments, 3–5 March, Long Beach, CA, USA

    Google Scholar 

  11. Bitaraf M, Ozbulut OE, Hurlebaus S, Barroso L (2010) Application of semi-active control strategies for seismic protection of buildings with MR dampers. Eng Struct 32:3040–3047

    Article  Google Scholar 

  12. Box G, Tiao, GC (1992) Bayesian inference in statistical analysis, Reading, MA

    Google Scholar 

  13. Casciati F, Cimellaro GP, Domaneschi M (2008) Seismic reliability of a cable-stayed bridge retrofitted with hysteretic devices. Comput Struct 86:1769–1781

    Article  Google Scholar 

  14. 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 

  15. 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 

  16. Choe K, Baruh H (1993) Sensor failure detection in flexible structures using model observers. J Dyn Sys Meas Cont 115:411–418

    Article  MATH  Google Scholar 

  17. 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 

  18. Ditlevsen O, Madsen HO (1996) Structural reliability methods. Wiley, New York

    Google Scholar 

  19. Dyke SJ, Spencer BF, Sain MK, Carlson JD (1996) Modeling and control of magnetorheological dampers for seismic response reduction. Smart Mat Struct 5:565–575

    Article  Google Scholar 

  20. Fernandez JA, Rix GJ (2006) Soil attenuation relationships and seismic hazard analyses in the upper Mississippi embayment. In: 8th US national conference on earthquake engineering 8NCEE, San Francisco, California. http://geosystems.ce.gatech.edu/soil_dynamics/research/groundmotionsembay/

  21. Gardoni P, Der Kiureghian A, Mosalam KM (2002) Probabilistic capacity models and fragility estimates for RC columns based on experimental observations. ASCE J Eng Mech 128(10):1024–1038

    Article  Google Scholar 

  22. Geweke J (1992) Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Bayesian Stat 4:164–193

    MathSciNet  Google Scholar 

  23. Hueste MBD, Bai J-W (2007) Seismic retrofit of a reinforced concrete flat-slab structure: part I-seismic performance evaluation. Eng Struct 29:1165–1177

    Article  Google Scholar 

  24. Hueste MBD, Bai J-W (2007) Seismic retrofit of a reinforced concrete flat-slab structure: part II-seismic fragility analysis. Eng Struct 29:1178–1188

    Article  Google Scholar 

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

    Google Scholar 

  26. Hurlebaus S, Gaul L (2006) Smart structure dynamics. Mech Syst Signal Process 20:255–281

    Article  Google Scholar 

  27. Jansen LM, Dyke SJ (2000) Semiactive control strategies for MR dampers: comparative study, ASCE. J Eng Mech 126:795–803

    Article  Google Scholar 

  28. Kim DH, Seo SN, Lee IW (2004) Optimal neurocontroller for nonlinear benchmark structure. ASCE J Struct Eng 130(4):424–429

    Google Scholar 

  29. Kim Y, Langari R (2007) Nonlinear identification and control of a building structure with a magnetorheological damper system. In: American control conference, New York, 11–13 July

    Google Scholar 

  30. Kim Y, Hurlebaus S, Sharifi R, Langari R (2009) Nonlinear identification of MIMO smart structures asme dynamic systems and control conference hollywood, California, 12–14 Oct

    Google Scholar 

  31. 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 

  32. 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 

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

    Article  Google Scholar 

  34. 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 

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

    MathSciNet  MATH  Google Scholar 

  36. 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 

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

    Article  Google Scholar 

  38. Laine M (2008) Adaptive MCMC methods with applications in environmental and geophysical models. Ph.D. dissertation. Lappeenranta University of Technology, Lappeenranta (Finland)

    Google Scholar 

  39. Li Z, Koh BH, Nagarajaiah S (2007) Detecting sensor failure via decoupled error function and inverse input-output model. ASCE J Eng Mech 133(11):1222–1228

    Article  Google Scholar 

  40. 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

  41. 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 

  42. 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 

  43. 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 

  44. Reigles DG, Symans MD (2006) Supervisory fuzzy control of a base-isolated benchmark building utilizing a neuro-fuzzy model of controllable fluid viscous dampers. Struct Cont Health Monit 13(2–3):724–747

    Article  Google Scholar 

  45. 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

  46. Spencer BF, Dyke SJ, Sain MK, Carlson JD (1997) Phenomenological model for magnetorheological dampers. ASCE J Eng Mech 123:230–238

    Article  Google Scholar 

  47. Taylor E (2007) The development of fragility relationships for controlled structures. MS thesis, Department of Civil Engineering, Washington University

    Google Scholar 

  48. Wen YK, Ellingwood BR, Bracci JM (2004) Vulnerability function framework for consequence-based engineering. Mid-America earthquake center project DS-4 report, Urbana, IL

    Google Scholar 

  49. Yoshida O, Dyke SJ (2004) Seismic control of a nonlinear benchmark building using smart dampers. ASCE J Eng Mech 130(4):386–392

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yeesock Kim .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Kim, Y., Bai, JW. (2017). Seismic Fragility Analysis of Faulty Smart Structures. 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_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47798-5_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47796-1

  • Online ISBN: 978-3-319-47798-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics