Field monitoring and numerical simulation of the thermal actions of a supertall structure

Su, Jia Zhan; Xia, Yong; Ni, Yiqing; Zhou, Lin; Su, Cheng

doi:10.1002/stc.1900

Cited by 20 publications

(21 citation statements)

References 18 publications

(25 reference statements)

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“…For example, the in-construction monitoring of the Burj Khalifa tower ensured the construction accuracy of the structure during the construction through surveying and elevation compensation. 30 The long-term temperature data of the Canton Tower 110 shorten the gap in the Chinese design code regarding the temperature loading of supertall structures. 95 Field measurements of the wind effects on a 420-m-tall supertall building in Hong Kong verified the wind characteristics used in the wind-tunnel testing and FE analysis, and the wind-resistant design of high-rise structures.…”

Section: Challenges Discussion and Conclusionmentioning

confidence: 99%

“…(iii) The internal forces of the highest stories were larger than those of the lower stories, which is different from the wind loading effect that generally F I G U R E 6 Temperature-induced responses of Canton Tower. 110 causes the largest force in the bottom of the structure. This study provided first-hand data for the design of supertall structures in the tropical region of China.…”

Section: Temperature-induced Deflection and Stressmentioning

confidence: 99%

See 1 more Smart Citation

Review on field monitoring of high‐rise structures

Xia

Weng

2020

Struct Control Health Monit

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Structural health monitoring (SHM) has been developed and applied to bridge structures since the early 1980s. Numerous approaches and techniques have been proposed and applied to supertall structures during the recent decade. This paper reviews the SHM techniques and applications in supertall structures. The vibration analysis techniques, seismic effect monitoring, wind effect monitoring, comfort assessment, temperature effect monitoring, and construction monitoring of high-rise structures are described and summarised. The latest developments in codification and standardisation in SHM are also introduced.

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Section: Challenges Discussion and Conclusionmentioning

confidence: 99%

Section: Temperature-induced Deflection and Stressmentioning

confidence: 99%

Review on field monitoring of high‐rise structures

Xia

Weng

2020

Struct Control Health Monit

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“…The temperature data of sections 3 and 8 are selected because they represent levels with and without functional floors, respectively. Su et al (2017a) showed that the temperature measured by the thermistors inside the CFT columns could be considered the effective temperature of the composite section of the column. In section 3, the averaged temperature of the inward and outward surfaces of the CFT columns is used as no thermistor was installed inside the CFT columns in the section.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…Ni et al (2011) developed monitoring-based temperature distribution models of Guangzhou Tower in order to serve as a reliable input for numerical simulation of the temperature-induced deformations. Su et al (2017a) inputted the structural temperature of the 600-m-tall Canton Tower into an FE model of the global structure and calculated the temperature-induced deformation. They then used the model to separate the typhoon-induced quasi-static responses from the measured displacement (Su et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Temperature-induced displacement of supertall structures: A case study

Hou

Xia

et al. 2018

Advances in Structural Engineering

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For a supertall structure, the temperature effect is an important factor that must be considered during the design, construction, and performance assessment. In this article, temperature-induced displacement of the 600-m-high Canton Tower is studied by the data-driven approach based on the sufficient real measurement data obtained from the structural health monitoring system. A multiple linear regression model is employed to establish the quantitative relation between the displacement and temperature data at different facades and sections of the structure in different seasons. Results show that an ordinary linear regression model is able to fit the monitoring data well. However, the model fails to interpret the physical meaning of the model coefficients. A regularized linear regression model is then employed and validated to describe the temperature-induced displacement of the structure. The physical relationship between the temperature and displacement is provided. Finally, the model is also used to separate the temperature- and wind-induced displacement of the supertall structure.

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“…10 On the one hand, the real geographical environment, terrain roughness, structural shape, and so forth are considered by SHM system, which ensures the reliability of the data. 11 On the other hand, the data can be collected in real time by SHM system, which achieves the integrity of the data. 12 Rubert et al 13 found a strategy to underpin a long-term wind farm lifetime based on SHM.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

Ding

Wan

2020

Struct Control Health Monit

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Wind speed forecasting can serve a wide spectrum of purposes, including scheduling of a power system and dynamic control of structures. A lot of models are widely used to forecast wind speed, consisting of deterministic models (e.g., physical models, statistical models, and artificial intelligence models) and probabilistic models (e.g., Bayesian model). The wind speed has the characteristics of random, nonlinear, and uncertainty, which highlights the importance of using Bayesian model to predict the wind speed. In this study, a Bayesian emulator with Gaussian process prior is adopted for probabilistic forecast of wind speed. The present Bayesian emulator approach not only maintains the data-driven property which guarantees its high flexibility in modeling the complexity of the target system but also allows for the efficient, probabilistic evaluation of the wind speed in terms of the predictive mean and variance. Nevertheless, the modeling performance of the Bayesian emulator directly depends on the selected covariance function. The influence of different types of covariance functions, which include squared-exponential (SE) covariance function, Matern (MA) covariance function, periodic (PE) covariance function, and composite covariance function, on forecasting performance of the wind speed is studied. One-month wind monitoring data collected by structural health monitoring (SHM) system installed on Jiubao bridge are employed to demonstrate the effectiveness of Bayesian emulator for forecasting the wind speed.

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Field monitoring and numerical simulation of the thermal actions of a supertall structure

Cited by 20 publications

References 18 publications

Review on field monitoring of high‐rise structures

Review on field monitoring of high‐rise structures

Temperature-induced displacement of supertall structures: A case study

Probabilistic forecast of wind speed based on Bayesian emulator using monitoring data

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