Operational modal identification of a boat-shaped building by a Bayesian approach

Lam, Heung-Fai; Zhang, Feng‐Liang; Hu, Jun

doi:10.1016/j.engstruct.2017.02.003

Cited by 45 publications

(19 citation statements)

References 41 publications

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“…On the basis of this method, the most probable values (MPVs) of the modal parameters and the associated posterior uncertainty could be determined. The method is briefly introduced as follows …”

Section: Methodsologymentioning

confidence: 99%

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Structural health monitoring of a 250‐m super‐tall building and operational modal analysis using the fast Bayesian FFT method

Zhang

Yang

Xiong

et al. 2019

Struct Control Health Monit

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This paper presents the work on the structural health monitoring design and operational modal analysis of a 250-m super-tall building situated in Shanghai, China. The building is a steel-concrete composite structure with a steel composite frame-concrete core tube system. At the 21st and 36th-38th floors, outrigger trusses and ring-shaped trusses are set to strengthen this structure.Because the height of this structure is overlimited and its lateral stiffness in the vertical direction is nonuniform, a SHM system was designed to monitor the structural condition and evaluate its safety. The SHM system is presented in this paper, and the instrumented equipment includes accelerometers and tilt sensors. The locations of sensors were well arranged so that SHM could be conducted using the least amount of sensors. On the basis of the system, an ambient vibration test was carried out to perform the analysis with four setups designed to investigate the modal parameters in the X and Y directions. The Fast Bayesian FFT method was employed to perform the operational modal analysis. The first 10 modes were identified. The modal parameters obtained by the Bayesian method are studied and discussed, and they are compared with the results obtained by other methods. Long-term monitoring of this super-tall building was also carried out to investigate the change of modal parameters in different stages. The results of this study are expected to provide a reference for model updating, damage detection, and SHM of this high-rise building.

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Section: Methodsologymentioning

confidence: 99%

“…Bayesian methods provide an important means to determine the most probable values of parameters and quantify the associated posterior uncertainty . They have been applied to many kinds of structures, for example, buildings, bridges, and towers, and extended to analyze free and forced vibration data.…”

Section: Introductionmentioning

confidence: 99%

Structural health monitoring of a 250‐m super‐tall building and operational modal analysis using the fast Bayesian FFT method

Zhang

Yang

Xiong

et al. 2019

Struct Control Health Monit

View full text Add to dashboard Cite

show abstract

“…The building is not an isolated structure; it is connected to an adjacent building from 4/F to 5/F. Measurement was performed in the three staircases and the 19th floor to cover a total of 57 measurement locations (Lam et al, 2017b). Six force-balance tri-axial accelerometers were used in the 15-setup test to obtain measurement data.…”

Section: Ambient Vibration Test and Experimental Resultsmentioning

confidence: 99%

Bayesian model updating of a 20-story office building utilizing operational modal analysis results

Lam

Adeagbo

2019

Advances in Structural Engineering

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Most existing buildings are not equipped with long-term monitoring system. For the structural model updating and damage detection of this type of structures, ambient vibration test is popular as artificial excitation is not required. This article presents in detail the full-scale ambient vibration test, operational modal analysis, and model updating of a tall building. To capture the dynamic properties of the target 20-story building with limited number of sensors, a 15-setup ambient vibration test was designed to cover at least three measurement points (each consists of a vertical and two orthogonal horizontal measured degrees of freedom) for each selected floor. The modal parameters of each setup were extracted from the measured acceleration signals using a frequency domain decomposition method and were combined to form the global mode shape through the least-squares method. Due to the regularity of the building, a simple class of shear building models was employed to capture the dynamic characteristics of the building under lateral vibration. The identified modal parameters of the building were employed for the model updating of the shear building model to identify the distribution of inter-story stiffness. Since the “amount” of the measured information is small when compared to the “amount” of required information for identifying the uncertain parameters, the model updating problem is unidentifiable. To handle this problem, the Markov chain Monte Carlo–based Bayesian model updating method is employed in this study. The identified modal parameters revealed interesting features about the dynamic properties of the building. The well-matched results between model-predicted and identified modal parameters show the validity of the shear building model in this case study. This study provides valuable experience in the area of structural model updating and structural health monitoring.

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“…It has been extended to analyze forced vibration data and free vibration data . This method has been applied in many field studies, for example, super‐tall building, primary‐secondary structure, coupled‐floor slab, bridge, Dagoba, and high‐rise buildings …”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Lam

2017

Struct Control Health Monit

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Structural model updating aims at calculating the in-situ structural properties (e.g., stiffness and mass) based on measured responses. One common approach is to first identify the modal parameters (i.e., natural frequencies and mode shapes) and then use them to update the structural parameters. In reality, the degrees of freedom that can be measured are usually limited by number of available sensors and accessibility of targeted measurement locations. Then, multiple setups are designed to cover all the degrees of freedom of interest and performed sequentially. Conventional methods do not account for identification uncertainty, which becomes critical when excitation information is not available. This is the situation in model updating utilizing ambient vibration data, in which the excitations, such as wind, traffic, and human activities, are random in nature and difficult to be measured. This paper develops a Bayesian model updating method incorporating modal identification information in multiple setups.Based on a recent fundamental two-stage Bayesian formulation, the posterior uncertainty of modal parameters is incorporated into the updating process without heuristics that are commonly applied in formulating the likelihood function. Synthetic and experimental data are used to illustrate the proposed method.

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Operational modal identification of a boat-shaped building by a Bayesian approach

Cited by 45 publications

References 41 publications

Structural health monitoring of a 250‐m super‐tall building and operational modal analysis using the fast Bayesian FFT method

Structural health monitoring of a 250‐m super‐tall building and operational modal analysis using the fast Bayesian FFT method

Bayesian model updating of a 20-story office building utilizing operational modal analysis results

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

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