Monitoring of wind effects on an instrumented low-rise building during the landfall of a severe tropical storm

Li, Q.S.; Wang, Y.J.; Li, J.C.

doi:10.1002/stc.1917

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…A pressure tubing system with the antirain function was used to convey the wind‐induced pressures on building cladding to a pressure transducer. Detailed description about the tubing system can be found elsewhere . The magnitude of the transfer function of the tube system was close to unity (±5% error), and the phase response was linear up to approximately 30 Hz.…”

Section: Field Measurement Facilitymentioning

confidence: 87%

Monitoring of wind pressures on gable roof of a low‐rise building during tropical cyclones and comparisons with wind tunnel test results

2019

Struct Control Health Monit

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A field monitoring program on an experimental low-rise building with gable roof has been established and implemented at a typhoon-prone area in China. This paper presents the field measurements obtained during two tropical cyclones in 2009. The characteristics of near-ground winds during the tropical cyclones are reported and discussed, including wind velocity spectra and profiles of mean wind speed, turbulence intensity, and turbulence integral length scale. This paper focuses on analysis of the external surface pressures measured on the central cross section of the gable roof for winds normal to the ridgeline. Pressure coefficients, probability density functions, and power spectra of the measured wind pressures are analyzed in detail. In addition, wind tunnel experiment of pressure measurements on a 1:50-scaled model of the experimental building is conducted. Comparison of the field measurements and the model test results is then performed. The paper aims to provide reliable fullscale measurements for further understanding of wind pressures on the roof of a typical low-rise building during tropical cyclones and as benchmarks for comparisons with wind tunnel experimental and numerical simulation results.

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Section: Field Measurement Facilitymentioning

confidence: 87%

Monitoring of wind pressures on gable roof of a low‐rise building during tropical cyclones and comparisons with wind tunnel test results

2019

Struct Control Health Monit

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“…Forecast of the wind speed is very important for characterization of wind effect on structures in advance. 17 The key point is to establish a suitable model to predict wind speed, which owns nature of nonstationarity and uncertainty, based on SHM data.…”

Section: Introductionmentioning

confidence: 99%

“…Zhou and Sun 16 studied the effects of high winds on vibrational responses and model parameters of a unique sea‐crossing cable‐stayed bridge based on the long‐term SHM data. Forecast of the wind speed is very important for characterization of wind effect on structures in advance 17 . The key point is to establish a suitable model to predict wind speed, which owns nature of nonstationarity and uncertainty, based on SHM data.…”

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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“…Li et al [18] investigated the characteristics of tropical cyclone-generated winds and evaluated the wind effects on a typical low-rise building under tropical cyclone conditions through field measurements.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

Jiang

2018

Shock and Vibration

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This paper summarizes a comprehensive study for non-Gaussian properties of wind pressure. The field measurements are implemented on the structure surface of a rigid structure, while a large-span membrane structure is selected as the flexible structure wind pressure contrast group. The non-Gaussian characteristics of measured pressure data were analyzed and discussed through probability density distribution, characteristic statistical parameters, power spectral density function, and the correlations, respectively. In general, the non-Gaussian characteristics of wind pressure present depending on tap location, wind direction, and structure geometry. In this study, the fluctuating wind pressures on the windward side and leeward side of the structures show obvious different degrees of non-Gaussian properties; that is, the surface of rigid structure shows strong non-Gaussian property in leeward, while the roof of flexible structure shows obvious non-Gaussian property in windward. Finally, this paper utilizes the present autospectrum empirical formula to fit the wind pressure power spectrum density of the measured data and obtains the conclusion that the existing empirical formula is not ideal for fitting of the flexible structure.

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Monitoring of wind effects on an instrumented low-rise building during the landfall of a severe tropical storm

Cited by 6 publications

References 15 publications

Monitoring of wind pressures on gable roof of a low‐rise building during tropical cyclones and comparisons with wind tunnel test results

Monitoring of wind pressures on gable roof of a low‐rise building during tropical cyclones and comparisons with wind tunnel test results

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

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

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