SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data

Ni, Yiqing; Xia, Yunxia; Lin, Wen Wei; Chen, W.H.; Ko, J. M.

doi:10.12989/sss.2012.10.4_5.411

Cited by 91 publications

(63 citation statements)

References 19 publications

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“…The detailed information of the Canton Tower can be found in Ni et al. (). In this study, the entire tower is modeled as an eight‐DOF lumped mass reduced‐order model.…”

Section: Application To the Field Measurement Data Of Canton Towermentioning

confidence: 99%

Identifiability‐Enhanced Bayesian Frequency‐Domain Substructure Identification

Yuen

Huang

2018

Computer aided Civil Eng

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Identification of structural parameters for large structures is challenging due to the large number of unknowns and its possibly ill‐conditioned nature. Substructure identification provides a feasible solution to partition a structure into substructures to tackle these difficulties and it has received considerable attention in the recent decades. Although there are variants of substructure identification methods, they share the key to back‐calculate the boundary force of the substructure using response measurements. It will then be treated as part of the input to the substructure. However, one major drawback is the deterioration of the identifiability of the inverse problem due to the reduction of effective constraints and the complicated interdependence relationship between the model parameters and the boundary force. In this article, an improved Bayesian substructure identification approach is proposed without the requirement of input or boundary force measurements. The crux is to model the boundary force as filtered white noise because it is an internal response of the system. This provides extra constraints to enhance the identifiability of the inverse problem. Furthermore, the proposed approach provides not only the most probable values of the identified parameters but also their associated uncertainties. This is an important indicator of identifiability, especially for this case. Examples of a 100‐story building are presented to demonstrate the proposed method. Comparison is also presented to confirm that the proposed method resolves the ill‐conditioned problem encountered in absolute acceleration measurements using existing methods.

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“…The detailed information of the Canton Tower can be found in Ni et al. (). In this study, the entire tower is modeled as an eight‐DOF lumped mass reduced‐order model.…”

Section: Application To the Field Measurement Data Of Canton Towermentioning

confidence: 99%

Identifiability‐Enhanced Bayesian Frequency‐Domain Substructure Identification

Yuen

Huang

2018

Computer aided Civil Eng

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“…21 A set of ambient vibration, temperature, wind speed and wind direction measurement data has been extracted from the in-construction health monitoring system installed on Canton Tower. 21 A set of ambient vibration, temperature, wind speed and wind direction measurement data has been extracted from the in-construction health monitoring system installed on Canton Tower.…”

Section: Deployment Of Shm Systemmentioning

confidence: 99%

“…21 A, scheme of data acquisition system; B, directions of measurements of acceleration and channel labels 2 MEASURED DATA FOR THIS STUDY 21 A, scheme of data acquisition system; B, directions of measurements of acceleration and channel labels 2 MEASURED DATA FOR THIS STUDY…”

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confidence: 99%

Model updating and variability analysis of modal parameters for super high‐rise structure

Chen

2018

Concurrency and Computation

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For Structural Health Monitoring (SHM), a core prerequisite is to accurately identify the modal properties. Since the dynamic characteristics of a structure change under the effects of different environmental factors such as temperature and wind, it is necessary to determine how the environmental factors affect the modal properties. This study focuses on the Guangzhou new TV tower (GNTVT), which is a 610-m high structure. Based on the measured data from GNTVT, both the varying patterns of the operational modal parameters under the effects of different environmental factors and the Finite Element Model (FEM) updating based on Genetic Algorithm (GA) had been investigated. Then, using the updated FEM, the effect of the temperature on the modal frequency had been investigated under two situations: (1) Temperature only affects the modulus of elasticity. This situation was considered to be more important because of the significant frequency change. When temperature increases by10 • C, the maximum change of modal frequencies is 1%. (2) Temperature only affects the geometric stiffness of structure. This situation was considered to be less important because the change in the frequency is very small which may be ignored.When temperature increases by10 • C, the maximum change of modal frequencies is 0.02%. The simulated results were consistent with the measured results. KEYWORDS finite element model, modal frequency, model update, super high-rise structure, temperature INTRODUCTIONUsing the vibration-based method, the primary task of Structural Health Monitoring (SHM) is to accurately identify the structural modal properties. This is because the modal properties, eg, frequency, modal shape, and damping ratio, can accurately reflect the structural properties such as stiffness and damage. 1,2 However, changes in the environmental conditions may cause changes in these modal properties. Over the past two decades, using field measurements and dynamic tests, the effects of the environmental factors, such as temperature, wind speed, and humidity on the structural modal properties have been extensively investigated. 3-17 While the vibration-based structural damage identification approaches have gained increasing attention in research, earlier studies have shown that the environmental effect is one of the main pitfalls limiting successful applications of the vibration-based damage detection methods to real structures. [18][19][20] These studies concluded that: (1) the effect of the variations in the modal properties due to the changes in the environmental factors is highly significant. The effect may be even greater than that caused by structural damage.(2) Temperature is the environmental factor that causes severe variation in modal properties.(3) The effect of the temperature on the natural frequencies can be eliminated using intelligent artificial algorithms such as Artificial Neural Networks (ANN) and Supporting Vector Machine (SVM).The studies mentioned in the preceding literature review are mostly based on the monitoring data ...

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“…As of today, the SHM system has successfully recorded the dynamic responses of the structure under several severe events, such as earthquakes and typhoons. Sets of ambient data have been adopted to verify the correctness of the full-order and reduced-order models in the previous study [43]. Particular wind speed data and wind rose diagrams drawn based on the monitor on top of the main tower during typhoons are shown in Figure 5.…”

Section: Shm System and Benchmark Model Of The Canton Towermentioning

confidence: 99%

“…Since the purpose of this paper will focus mainly on the global dynamic responses of the structure, the reduced order FE model is adopted in this study. The reduced-order model has been validated in the previous study [43] by field test data to have great precision and can describe the dynamic characteristic of the structure well. In the reduced-order model, the whole structure is modeled as 37 beam elements (27 for the main tower and 10 for the mast) and 38 nodes.…”

Section: Shm System and Benchmark Model Of The Canton Towermentioning

confidence: 99%

PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations

2017

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A pounding tuned mass damper (PTMD) is introduced by making use of the energy dissipated during impact. In the proposed PTMD, a viscoelastic layer is attached to an impact limitation collar so that energy can be further consumed and transferred to heat energy. An improved numerical model to simulate pounding force is proposed and verified through experimentation. The accuracy of the proposed model was validated against a traditional Hertz-based pounding model. A comparison showed that the improved model tends to have a better prediction of the peak pounding force. A simulation was then carried out by taking the benchmark Canton Tower, which is a super-tall structure, as the host structure. The dynamic responses of uncontrolled, TMD-controlled and PTMD controlled system were simulated under wind and earthquake excitations. Unlike traditional TMDs, which are sensitive to input excitations and the mass ratio, the proposed PTMD maintains a stable level of control efficiency when the structure is excited by different earthquake records and different intensities. Particularly, more improvement can be observed when an extreme earthquake is considered. The proposed PTMD was able to achieve similar, or even better, control effectiveness with a lower mass ratio. These results demonstrate the superior adaptability of the PTMD and its applicability for protection of a building against seismic activity. A parametric study was then performed to investigate the influence of the mass ratio and the gap value on the control efficiency. A comparison of results show that better control results will be guaranteed by optimization of the gap value.

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SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data

Cited by 91 publications

References 19 publications

Identifiability‐Enhanced Bayesian Frequency‐Domain Substructure Identification

Identifiability‐Enhanced Bayesian Frequency‐Domain Substructure Identification

Model updating and variability analysis of modal parameters for super high‐rise structure

PTMD Control on a Benchmark TV Tower under Earthquake and Wind Load Excitations

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