Bayesian structural model updating using ambient vibration data collected by multiple setups

Zhang, Feng‐Liang; Ni, Yan‐Chun; Lam, H. F.

doi:10.1002/stc.2023

Cited by 41 publications

(28 citation statements)

References 47 publications

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“…In the future work, the behaviors outside of the linear elastic range would be more interesting for the comparison when the data collected subjected to typhoon or earthquake excitation are available. Furthermore, model updating of the super tall building by a Bayesian approach incorporating multiple setups data will also be carried out.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the dynamic characteristics of a super tall building using data from ambient vibration and shake table tests by a Bayesian approach

Zhang

Ventura

Xiong

et al. 2017

Struct Control Health Monit

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Summary The Shanghai Tower is a newly built 127‐story and 632 m high super tall building. As of April 2017, it was ranked as the second tallest building in the world. Its main structural system is a mega‐frame‐tube‐outrigger system with six outrigger trusses along the height. Due to its unique structural configuration, a series of field and laboratory model tests have been conducted to better understand its dynamic characteristics. Before its construction, a scaled model of the tower was tested on a shake table, and the results were used to refine the design of the tower. At the completion of the construction, full‐scale ambient vibration tests were performed. A Bayesian method was used to perform operational modal analysis from the shake table and full‐scale ambient vibration tests. The most probable value of the modal parameters and the associated posterior uncertainties were calculated using this method. The first eight modes were identified, including three translational modes in each principal direction and two torsional modes. Using these results, the dynamic characteristics and associated uncertainties obtained from the two tests were investigated and compared in this paper. Due to the scaling of the model, there are some discrepancies between the natural frequencies obtained from two different tests, but the identified mode shapes matched very well. Although the structure was designed in a very innovative manner, its dynamic characteristics are similar to regular tall buildings. The results from this investigation provide valuable information for an ongoing condition assessment of this super tall building.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the dynamic characteristics of a super tall building using data from ambient vibration and shake table tests by a Bayesian approach

Zhang

Ventura

Xiong

et al. 2017

Struct Control Health Monit

Self Cite

View full text Add to dashboard Cite

show abstract

“…of Eqs. (20) and (22) can be subtle for a sufficiently large number of data sets. In this case, we can compute only the integration in Eq.…”

Section: Joint Posterior Distributionmentioning

confidence: 99%

“…Let In summary, quantification of the uncertainty associated with the hyper-parameters, as well as the data-set-specific dynamical parameters can be accomplished based on Eqs. (20), (25), and (26).…”

Section: Posterior Predictive Distributionmentioning

confidence: 99%

“…This needs to draw samples of the hyper-parameters from the probability distributions specified in either Eq. (20) or (22). We demonstrate the sampling method only for the former case as the latter can be accomplished likewise.…”

Section: Mcmc Samplingmentioning

confidence: 99%

“…Propagate the uncertainty for unobserved system operating conditions 3.1. Compute the hyper-parameters marginal distribution ( ) MAP estimations of the hyper-parameters In this section, an efficient method is proposed to compute the hyper-parameters MAP estimations.This necessitates maximizing the hyper-parameters marginal distribution given in Eq (20)…”

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confidence: 99%

See 2 more Smart Citations

Hierarchical Bayesian operational modal analysis: Theory and computations

Sedehi

Katafygiotis

Papadimitriou

2020

Mechanical Systems and Signal Processing

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This paper presents a hierarchical Bayesian modeling framework for the uncertainty quantification in modal identification of linear dynamical systems using multiple vibration data sets. This novel framework integrates the state-of-the-art Bayesian formulations into a hierarchical setting aiming to capture both the identification precision and the ensemble variability prompted due to modeling errors. Such cutting-edge developments have been absent from the modal identification literature, sustained as a long-standing problem at the research spotlight. Central to this framework is a Gaussian hyper probability model, whose mean and covariance matrix are unknown encapsulating the uncertainty of the modal parameters. Detailed computation of this hierarchical model is addressed under two major algorithms using Markov chain Monte Carlo (MCMC) sampling and Laplace asymptotic approximation methods. Since for a small number of data sets the hyper covariance matrix is often unidentifiable, a practical remedy is suggested through the eigenbasis transformation of the covariance matrix, which effectively reduces the number of unknown hyper-parameters. It is also proved that under some conditions the maximum a posteriori (MAP) estimation of the hyper mean and covariance coincide with the ensemble mean and covariance computed using the MAP estimations corresponding to multiple data sets. This interesting finding addresses relevant concerns related to the outcome of the mainstream Bayesian methods in capturing the stochastic variability from dissimilar data sets. Finally, the dynamical response of a prototype structure tested on a shaking table subjected to Gaussian white noise base excitation and the ambient vibration measurement of a cable footbridge are employed to demonstrate the proposed framework.

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Operational modal identification and finite element model updating of a coupled building following Bayesian approach

Lam

Yang

2017

Struct Control Health Monit

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Summary This paper presents a comprehensive study of the full‐scale ambient vibration test, modal analysis, finite element (FE) modeling, and model updating of a coupled building in Hong Kong. The coupled building comprised a main part and a complementary part. To capture the dynamic properties of the building, a 21‐setup ambient vibration test was designed and conducted. The modal parameters of each setup were identified following a fast Bayesian fast Fourier transform approach, and the partial mode shapes from the different setups were assembled following the least squares method. The identified modal parameters were analyzed and discussed in detail, revealing certain features of the coupling effects between the main and complementary parts. To determine the equivalent Young's moduli of various structural components, an FE model of the coupled building was developed and updated with the identified modal parameters. The Bayesian approach was followed to explicitly handle the uncertainties induced by modeling error and measurement noise. To ensure the model updating method is applicable even in unidentifiable cases, a Markov chain Monte Carlo simulation was employed in the proposed method to generate samples for approximating the posterior probability density functions of uncertain model parameters. The close match between the modal parameters calculated from the updated FE model and those identified from the measured time‐domain data verified the validity of the proposed FE model. This study provides valuable experience and information for the development of structural model updating and structural health monitoring of building systems.

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Bayesian structural model updating using ambient vibration data collected by multiple setups

Cited by 41 publications

References 47 publications

Evaluation of the dynamic characteristics of a super tall building using data from ambient vibration and shake table tests by a Bayesian approach

Evaluation of the dynamic characteristics of a super tall building using data from ambient vibration and shake table tests by a Bayesian approach

Hierarchical Bayesian operational modal analysis: Theory and computations

Operational modal identification and finite element model updating of a coupled building following Bayesian approach

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