Mode identifiability of a cable-stayed bridge based on a Bayesian method

Zhang, Feng‐Liang; Ni, Yiqing; Ni, Yan-Chun

doi:10.12989/sss.2016.17.3.471

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…For the six groups of data, the noise levels increased gradually. Figure 5a shows the modal signal to noise ratio (SNR) ( 26–29,44 ) from Data 1 to Data 6 of all the seven modes used for damage detection. It is seen that with the increase of noise levels, the modal SNRs in all the modes decrease obviously, while the COV of natural frequencies (Figure 5b), COV of damping ratios (Figure 5c), and the posterior uncertainty of mode shape (Figure 5d) increase consistently.…”

Section: Numerical Examplementioning

confidence: 99%

Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Zhang

Kim

Goi

2020

Struct Control Health Monit

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Section: Numerical Examplementioning

confidence: 99%

Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Zhang

Kim

Goi

2020

Struct Control Health Monit

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“…This is because of the so-called mode identifiability, which is controlled by the signal-to-noise of data during operational monitoring. 37 In this section, the recorded acceleration responses of the bridge deck for 20 days (January 1-20, 2010) are utilized to autonomously identify the modal parameters, after which the modal tracking procedure based on the pre-defined baseline list and k-means clustering is applied to track the identified values. 20 The identified and tracked modal frequencies of SCB are shown in Figure 12, where each mode is colored with one color.…”

Section: Application Of the Presented Framework For In-service Monimentioning

confidence: 99%

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

Mao

Wang

et al. 2019

Struct Control Health Monit

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Summary Obtaining timely information about the health of civil infrastructure is critical to ensuring safe and reliable operation. Structural health monitoring has been proposed as a means to provide such information; however, most structural health monitoring systems provide only raw data, rather than actionable information. It is necessary to develop automated modal analysis strategies that can provide near real‐time dynamic information regarding the in‐service state of a bridge, which is essential to vibration control, finite element model calibration, and damage detection for safety and serviceability condition assessment. This study presents an automated framework to extract structural modal parameters from the stabilization diagram using a parametric modal identification method such as stochastic subspace identification. The framework focuses on the automated modal analysis issues of an in‐service long‐span bridge with close‐frequency modes. The presented framework is validated using experimental tests of a 1.8‐m 18‐story laboratory model. Subsequently, data from Sutong Cable‐Stayed Bridge are employed to demonstrate its potential usage in the field. Finally, an application of the automated framework is presented to identify and track the modal parameters of the deck of Sutong Cable‐Stayed Bridge for 20 days. Results show that the presented framework can successfully extract the structural modal parameters with good accuracy and robustness, hence can provide a reliable technical support for in‐service monitoring of long‐span bridges.

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“…After model updating of the undamaged and possibly damaged structure, possible structural damage can then be identified by comparing the stiffness distributions of the structure at different statuses. Bayesian modal analysis has been successfully developed and applied in field tests . This paper focuses on the development in Bayesian structural model updating and damage detection.…”

Section: Introductionmentioning

confidence: 99%

Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection

Lam

Yang

2018

Struct Control Health Monit

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Summary This paper proposes a Bayesian method for structural model updating and damage detection using modal data. A recently developed Markov chain Monte Carlo algorithm is adopted to handle the model updating problem. The proposed Bayesian method focuses on calculation of the posterior probability distribution function of uncertain model parameters. In addition to the most probable values of the uncertain parameters, the associated uncertainties can be calculated with consideration of the effects of both the modeling error and the measurement noise. An experimental case study was carried out with a shear building model under laboratory conditions to study the identifiability of the model‐updating problem following the proposed Bayesian method. The results demonstrate the change in the posterior probability distribution function of the uncertain parameters with the amount of measured information. It also demonstrates the ability of the proposed method to handle unidentifiable problems. The proposed Bayesian method is then applied for structural damage detection by calculating the probability distribution of the extent of damage to various structural components. To demonstrate the proposed Bayesian damage‐detection method, ambient vibration tests were carried out on a 2‐story steel frame with bolted connections. Joint damage was simulated by loosening some bolts at the target beam‐column connection. The model‐updating results show that the uncertainty associated with the rotational stiffness of the steel joints was very high, rendering the problem almost unidentifiable. Although the problem is almost unidentifiable, the calculated probability distribution of the damage extent can still locate the damaged joint and estimate the damage extent (i.e., the percentage reduction in rotational stiffness) together with the associated uncertainty.

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Mode identifiability of a cable-stayed bridge based on a Bayesian method

Cited by 17 publications

References 26 publications

Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Efficient Bayesian FFT method for damage detection using ambient vibration data with consideration of uncertainty

Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge

Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection

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