Skip to main content

Sampling-Based Techniques for Finite Element Model Updating in Bayesian Framework Using Commercial Software

  • Conference paper
  • First Online:
Advances in Structural Technologies

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 81))

Abstract

Finite element (FE) model updating in Bayesian framework, using sampling-based techniques like Markov chain Monte carlo (MCMC), is observed to be investigated by many researchers. The present work is focussed on FE model updating using MCMC techniques where modelling is performed using commercial FE software to avoid the difficulties with writing computer program for FE modelling. In this present work, two prominent MCMC techniques based on Metropolis–Hastings (MH) algorithm, viz. enhanced-MCMC and transitional MCMC are primarily used, while FE modelling is performed using a well-known FE software, viz. SAP2000. A reasonably complex structure in the form of a cantilever plate is considered in this study and modelled using shell elements. Besides, damage is simulated in this plate structure by decreasing the Young’s modulus of few of the elements of the discretized plate structure. Modal data in the form of frequencies and incomplete mode shapes, evaluated from the damaged structure, are taken as the measured modal data. The technique of error localisation and an improved parameter selection method are adopted for limiting the number of updating parameters to facilitate better performance. Moreover, Gibbs sampling which is an effective algorithm of MCMC technique is also demonstrated using SAP2000. All the MCMC techniques for FE model updating are performed using a computational framework based on interactions between MATLAB and SAP2000 with the help of SAP2000 open application programming interface (OAPI). It is observed that level of performances in FE model updating is most satisfactory while using enhanced-MCMC in comparison with others.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
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. Friswell MI, Mottershead JE (1995) Finite element model updating in structural dynamics. Kluwer Academic Publishers, Boston

    Book  Google Scholar 

  2. Ewins DJ (2000) Adjustment or updating of models. Sadhana 25:235–245

    Article  Google Scholar 

  3. Mottershead JE, Friswell MI (2001) Physical understanding of structures by model updating. In: Proceedings of the international conference on structural identification, Kassel,Germany, pp 81–96

    Google Scholar 

  4. Mottershead JE, Friswell MI (1993) Model updating in structural dynamics: a survey. J Sound Vib 167:347–375

    Article  Google Scholar 

  5. Baruch M (1978) Optimization procedure to correct stiffness and flexibility matrices using vibration tests. AIAA J 16:1208–1210

    Article  Google Scholar 

  6. Baruch M (1982) Methods of reference basis for identification of linear dynamic structures. In: Proceedings of the 23rd structures, structural dynamics and material conference, New Orleans, Louisiana, May pp 557–563

    Google Scholar 

  7. Baruch M (1984) Methods of reference basis for identification of linear dynamic structures. AIAA J 22(4):561–564

    Article  MathSciNet  Google Scholar 

  8. Baruch M, Bar-Itzhack IY (1978) Optimal weighted orthogonalization of measured modes. AIAA J 16(4):346–351

    Article  Google Scholar 

  9. Berman A, Nagy EG (1983) Improvement of large analytical model using test data. AIAA J 21(8):1168–1173

    Article  Google Scholar 

  10. Caesar B (1986) Update and identification of dynamic mathematical models. In: Proceedings of the fourth international modal analysis conference, Los Angeles, pp 394–401

    Google Scholar 

  11. Caesar B (1987) Updating system matrices using modal test data. In: Proceedings of the fifth international modal analysis conference, London, England, April, pp 453–459

    Google Scholar 

  12. Wei FS (1990) Analytical dynamic model improvement using vibration test data. AIAA J 28(1):175–177

    Article  Google Scholar 

  13. Friswell MI, Inman DJ, Pilkey DF (1998) The direct updating of damping and stiffness matrices. AIAA J 36(3):491–493

    Article  Google Scholar 

  14. Mottershead JE, Link M, Friswell MI (2011) The sensitivity method in finite element model updating: a tutorial. Mech Syst Sig Process 25:2275–2296

    Google Scholar 

  15. Yang YB, Chen YJ (2010) Direct versus iterative model updating methods for mass and stiffness matrices. Int J Struct Stab Dyn 10:165–186

    Article  MathSciNet  Google Scholar 

  16. Lin RM, Ewins DJ (1994) Analytical model improvement using frequency response functions. Mech Syst Sig Process 8(4):437–458

    Article  Google Scholar 

  17. Imregun M, Visser WJ, Ewins DJ (1995) Finite Element model updating using frequency response function data—I theory and initial investigation. Mech Syst Sig Process 9(2):187–202

    Article  Google Scholar 

  18. Beck JL (1996) System identification methods applied to measured seismic response. In: Proceedings of the eleventh world conference on earthquake engineering

    Google Scholar 

  19. Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124(4):455–461

    Google Scholar 

  20. Vanik MW, Beck JL, Au SK (2000) Bayesian probabilistic approach to structural health monitoring. J Eng Mech 126(7):738–745

    Article  Google Scholar 

  21. Yuen KV (2006) An extremely efficient finite-element model updating methodology with applications to damage detection. In: Proceedings of enhancement and promotion of computational methods in engineering and science X, Sanya, Hainan, China, August 21–23, pp 166–179

    Google Scholar 

  22. Yuen KV, Beck JL, Katafygiotis LS (2006) Efficient model updating and health monitoring methodology using incomplete modal data without mode matching. Struct Control Health Monit 13:91–107

    Article  Google Scholar 

  23. Yuen KV (2010) Bayesian methods for structural dynamics and civil engineering. Wiley (Asia) Private Limited, Chichester, UK

    Google Scholar 

  24. Mustafa S, Debnath N, Dutta A (2015) Bayesian probabilistic approach for model updating and damage detection for a Large Truss Bridge. Int J Steel Struct

    Google Scholar 

  25. Das A, Debnath N (2018) A Bayesian finite element model updating with combined normal and lognormal probability distributions using modal measurements. Appl Math Model 61:457–483

    Article  MathSciNet  Google Scholar 

  26. Beck JL, Au SK (2002) Bayesian updating of structural models and reliability using Markov Chain Monte Carlo simulation. J Eng Mech 128(4):380–391

    Article  Google Scholar 

  27. Ching J, Chen YC (2007) Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J Eng Mech 133(7):816–832

    Article  Google Scholar 

  28. Cheung SH, Beck JL (2009) Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters. J Eng Mech 135(4):243–255

    Article  Google Scholar 

  29. Lam HF, Yang J, Au SK (2015) Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov chain Monte Carlo simulation algorithm. Eng Struct 102:144–155

    Article  Google Scholar 

  30. Lam HF, Hu J, Yang JH (2017) Bayesian operational modal analysis and Markov chain Monte Carlo-based model updating of a factory building. Eng Struct 132:314–336

    Article  Google Scholar 

  31. Lam HF, Alabi SA, Yang JH (2017) Identification of rail-sleeper-ballast system through time-domain Markov chain Monte Carlo-based Bayesian approach. Eng Struct 140:421–436

    Article  Google Scholar 

  32. Lam HF, Yang JH, Hu Q, Ng CT (2018) Railway ballast damage detection by Markov chain Monte Carlo-based Bayesian method. Struct Health Monit 17(3):706–724

    Article  Google Scholar 

  33. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092

    Article  Google Scholar 

  34. Hastings WK (1970) Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57(1):97–109

    Article  MathSciNet  Google Scholar 

  35. CSI Computer & Structures Inc. (2009) Three dimentional static and dynamic finite element analysis and design of structures. SAP2000, Computer & Structures, Inc., Berkeley, CA

    Google Scholar 

  36. Fissette E, Stavrinidis C, Ibrahim S (1988) Error location and updating of analytical dynamic models using a force balance method. In: Proceedings of the 6th international modal analysis conference, Kimssimmee, FL, pp 1183–1190

    Google Scholar 

  37. Kim GH, Park YS (2008) An automated parameter selection procedure for finite-element model updating and its applications. J Sound Vib 309:778–793

    Article  Google Scholar 

  38. Geman S, Geman D (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans Pattern Anal Mach Intell 6(6):721–741

    Article  Google Scholar 

  39. Ching J, Muto M, Beck JL (2006) Structural model updating and health monitoring with incomplete modal data using Gibbs sampler. Comput Aided Civ Infrastruct Eng 21:242–257

    Article  Google Scholar 

  40. Bansal S (2015) A new Gibbs sampling-based Bayesian model updating approach using modal data from multiple setups. Int J Uncertainty Quantif 5(4):361–374

    Article  Google Scholar 

  41. Cheung SH, Bansal S (2017) A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data. Mech Syst Sig Process 92:156–172

    Article  Google Scholar 

  42. MATLAB 2015a, The MathWorks, Inc.: 2015.

    Google Scholar 

  43. Bernardo JM, Smith AFM (2000) Bayesian theory. Wiley, England

    MATH  Google Scholar 

  44. O’Callahan J, Avitabile P, Riemer R (1989) System equivalent reduction expansion process (SEREP). IMAC VII:29–37

    Google Scholar 

  45. Gelman AB, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–472

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nirmalendu Debnath .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Das, A., Debnath, N. (2021). Sampling-Based Techniques for Finite Element Model Updating in Bayesian Framework Using Commercial Software. In: Adhikari, S., Dutta, A., Choudhury, S. (eds) Advances in Structural Technologies. Lecture Notes in Civil Engineering, vol 81. Springer, Singapore. https://doi.org/10.1007/978-981-15-5235-9_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5235-9_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5234-2

  • Online ISBN: 978-981-15-5235-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics