Engineering performance of two analytical methodologies for estimating modal parameter uncertainty for structures

Liang Su, Xin Huang, Jing quan Zhang, James Michael LaFave

Research output: Contribution to journalArticlepeer-review


Stochastic subspace identification (SSI) and Bayesian methods are now the main representative approaches for high-performance system identification and uncertainty quantification. Both methods have been extensively used in engineering applications for the last 10 years despite them having quite different views in principle. However, no comparison has been carried out to inform which method is actually better for engineering applications in terms of modal identification accuracy, uncertainty quantification, and so forth. This paper therefore investigates the differences between these two quantifications of parameter uncertainty in system identification. Synthetic data for a six-degree-of-freedom spring–mass numerical system are first studied to compare their identification accuracy and applicable conditions. The investigation is then extended to field data from actual structures, which involves ambient modal testing of Heritage Court Tower in Canada, Canton Tower in China, and Ting Kau Bridge in Hong Kong. Results from the synthetic data show that, under white-noise excitation, identified modal parameters and quantified uncertainty for both methods are highly consistent with the values of frequentist statistics. However, when the excitation is not white noise, there may be some spurious modes identified via SSI, and the uncertainty quantified under colored-noise excitation via SSI is almost always larger than those under white noise, while the Bayesian method is not disturbed. Results for the structural field data indicate that in general applications under environmental excitation the identified modal parameters from both methods are almost identical; the quantified uncertainty of the SSI method is slightly larger than that of the Bayesian method, but they are of the same order of magnitude and can meet engineering requirements.

Original languageEnglish (US)
Article numbere2752
JournalStructural Control and Health Monitoring
Issue number7
StatePublished - Jul 2021
Externally publishedYes


  • Bayesian method
  • modal parameters
  • statistical results
  • subspace identification
  • uncertainty quantification

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanics of Materials


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