Skip to main content
SpringerLink
Log in

Advances in Online Structural Identification

  • Reference work entry
  • First Online:
Encyclopedia of Earthquake Engineering

  • 346 Accesses

Synonyms

Bayesian inference; Extended Kalman filter; Model class selection; Model updating; Nonparametric identification; Online updating; Outlier, structural health monitoring; System identification

Introduction

Structural health monitoring using dynamic response measurement has received a tremendous attention over the last decades. A number of methods have been developed, including the novelty measure technique (Worden 1997), the GA-based substructural identification methods (Koh and Shankar 2003), and the evolutionary strategy (Franco et al. 2004). On the other hand, the Bayesian inference (Beck and Katafygiotis 1998; Beck 2010; Yuen 2010a) using probability logic provides a rigorous solution to parametric identification and uncertainty quantification for different problems in structural and geotechnical engineering, such as modal identification using nonstationary noisy response measurements (Yuen and Katafygiotis 2005; Yuen et al. 2006a), ambient vibration survey (Yuen and...

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,799.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 2,999.99
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

  • Beck JL (2010) Bayesian system identification based on probability logic. Struct Control Health Monit 17(7):825–847

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Beck JL, Yuen KV (2004) Model selection using response measurements: Bayesian probabilistic approach. J Eng Mech (ASCE) 130(2):192–203

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Durovic ZM, Kovacevic BD (1999) Robust estimation with unknown noise statistics. IEEE Trans Automat Control 44(6):1292–1296

    Article  MathSciNet  MATH  Google Scholar 

  • Franco G, Betti R, LuÅŸ H (2004) Identification of structural systems using an evolutionary strategy. J Eng Mech (ASCE) 130(10):1125–1139

    Article  Google Scholar 

  • Gull SF (1988) Bayesian inductive inference and maximum entropy. In: Skilling J (ed) Maximum entropy and Bayesian methods. Kluwer, Boston, pp 53–74

    Google Scholar 

  • Hoi KI, Yuen KV, Mok KM (2009) Prediction of daily averaged PM10 concentrations by statistical time-varying model. Atmos Environ 43(16):2579–2581

    Article  Google Scholar 

  • Hoi KI, Yuen KV, Mok KM (2011) Iterative probabilistic approach for selection of time-varying model classes. Procedia Eng 14:2585–2592. doi:10.1016/j.proeng.2011.07.325

    Article  Google Scholar 

  • Jazwinski AH (1970) Stochastic processes and filtering theory. Academic, New York

    MATH  Google Scholar 

  • Jeffreys H (1961) Theory of probability, 3rd edn. Clarendon, Oxford

    MATH  Google Scholar 

  • Kalman RE, Bucy R (1961) New results in linear filtering and prediction theory. J Basic Eng 83:95–108

    Article  MathSciNet  Google Scholar 

  • Katafygiotis LS, Beck JL (1998) Updating models and their uncertainties. II: model identifiability. J Eng Mech (ASCE) 124(4):463–467

    Article  Google Scholar 

  • Koh CG, Shankar K (2003) Substructural identification method without interface measurement. J Eng Mech (ASCE) 129(7):769–776

    Article  Google Scholar 

  • Lam HF, Yuen KV, Beck JL (2006) Structural health monitoring via measured Ritz vectors utilizing artificial neural networks. Comput-Aided Civil Infrastruct Eng 21(4):232–241

    Article  Google Scholar 

  • Mehra R (1970) On the identification of variances and adaptive Kalman filtering. IEEE Trans Automat Control 15:175–184

    Article  MathSciNet  Google Scholar 

  • Mu HQ, Yuen KV (2014) Novel outlier-resistant extended Kalman filter for robust online structural identification. J Eng Mech (ASCE). doi:10.1061/(ASCE)EM.1943-7889.0000810

    Google Scholar 

  • Muto M, Beck JL (2008) Bayesian updating and model class selection for hysteretic structural models using stochastic simulation. J Vib Control 14(1–2):7–34

    Article  MATH  Google Scholar 

  • Myers K, Tapley B (1976) Adaptive sequential estimation with unknown noise statistics. IEEE Trans Automat Control 21(4):520–523

    Article  MATH  Google Scholar 

  • Papadimitriou C, Fritzen C-P, Kraemer P, Ntotsios E (2011) Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. Struct Control Health Monit 18(5):554–573

    Article  Google Scholar 

  • Sorensen SW, Sacks JE (1971) Recursive fading memory filters. Inform Sci 3:101–119

    Article  MATH  Google Scholar 

  • Sorenson HW, Alspach DL (1971) Recursive Bayesian estimation using Gaussian sums. Automatica 7(4):465–479

    Article  MathSciNet  MATH  Google Scholar 

  • Worden K (1997) Structural fault detection using a novelty measure. J Sound Vib 201(1):85–101

    Article  MathSciNet  Google Scholar 

  • Worden K, Hensman JJ (2012) Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference. Mech Systems Signal Process 32:153–169

    Article  Google Scholar 

  • Yan WM, Yuen KV, Yoon GL (2009) Bayesian probabilistic approach for the correlations of compressibility index for marine clays. J Geotech Geoenviron Eng (ASCE) 135(12):1932–1940

    Article  Google Scholar 

  • Yuen KV (2010a) Bayesian methods for structural dynamics and civil engineering. Wiley, New York

    Book  Google Scholar 

  • Yuen KV (2010b) Recent developments of Bayesian model class selection and applications in civil engineering. Struct Safety 32:338–346

    Article  Google Scholar 

  • Yuen KV, Katafygiotis LS (2005) Model updating using response measurements without knowledge of the input spectrum. Earthq Eng Struct Dyn 34(2):167–187

    Article  Google Scholar 

  • Yuen KV, Katafygiotis LS (2006) Substructure identification and health monitoring using noisy response measurements only. Comput-Aided Civil Infrastruct Eng 21(4):280–291

    Article  Google Scholar 

  • Yuen KV, Kuok SC (2010a) Ambient interference in long-term monitoring of buildings. Eng Struct 32(8):2379–2386

    Article  Google Scholar 

  • Yuen KV, Kuok SC (2010b) Modeling of environmental influence in structural health monitoring assessment for reinforced concrete buildings. Earthq Eng Eng Vib 9(2):295–306

    Article  Google Scholar 

  • Yuen KV, Kuok SC (2011) Bayesian methods for updating dynamic models. Appl Mech Rev (ASME) 64(1):010802–1–010802–18

    Article  Google Scholar 

  • Yuen KV, Mu HQ (2011) Peak ground acceleration estimation by linear and nonlinear models with reduced order Monte Carlo simulation. Comput-Aided Civil Infrastruct Eng 26(1):30–47

    Google Scholar 

  • Yuen KV, Mu HQ (2012) A novel probabilistic method for robust parametric identification and outlier detection. Probab Eng Mech 30:48–59

    Article  Google Scholar 

  • Yuen KV, Beck JL, Katafygiotis LS (2006a) Efficient model updating and monitoring methodology using incomplete modal data without mode matching. Struct Control Health Monitor 13(1):91–107

    Article  Google Scholar 

  • Yuen KV, Beck JL, Katafygiotis LS (2006b) Unified probabilistic approach for model updating and damage detection. J Appl Mech (ASME) 73(4):555–564

    Article  MATH  Google Scholar 

  • Yuen KV, Hoi KI, Mok KM (2007) Selection of noise parameters for Kalman filter. Earthq Eng Eng Vib 6:49–56

    Article  Google Scholar 

  • Zhou J, Luecke RH (1994) Estimation of the covariances of the process noise and measurement noise for a linear discrete dynamic system. Comput Chem Eng 19(2):187–195

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science and Technology, University of Macau, Faculty of Science and Technology, Macau, China

    Ka-Veng Yuen

Authors
  1. Ka-Veng Yuen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ka-Veng Yuen .

Editor information

Editors and Affiliations

  1. Institute for Computer Science in Civil Engineering, Gottfried Wilhelm Leibniz, University Hannover, Hannover, Germany

    Michael Beer

  2. Department of Civil Engineering & Engineering Mechanics, Columbia University, New York, NY, USA

    Ioannis A. Kougioumtzoglou

  3. Institute for Risk & Uncertainty and Centre for Engineering Sustainability, Liverpool, UK

    Edoardo Patelli

  4. Institute for Risk & Uncertainty and Centre for Engineering Dynamics, Liverpool, UK

    Siu-Kui Au

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Yuen, KV. (2015). Advances in Online Structural Identification. In: Beer, M., Kougioumtzoglou, I.A., Patelli, E., Au, SK. (eds) Encyclopedia of Earthquake Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35344-4_84

Download citation

Publish with us

Policies and ethics

Search

Navigation