Statistical buffeting response of flexible bridges influenced by errors in aeroelastic loading estimation

Seo, Dong-Woo; Caracoglia, Luca

doi:10.1016/j.jweia.2012.03.036

Cited by 41 publications

(6 citation statements)

References 22 publications

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“…(10) and Eq. (11) [25,[33][34][35][36][37]: where , and represent lift, drag and moment from self-excited motions, respectively. Self-excited and buffeting forces are shown schematically in Fig.…”

Section: Calculation Of Buffeting Forcementioning

confidence: 99%

Buffeting performance of long-span suspension bridge based on measured wind data in a mountainous region

Yang¹,

Yao²,

Wei³

et al. 2018

J. vibroeng.

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Long-span suspension bridge increases rapidly in size as a result of bridge construction in a mountainous region, in addition, more and more long-span suspension bridges are in process of preparation. The bridge stiffness decreases with the increase of bridge span length, and hence the buffeting performance of bridge is sensitive to external factors. In this paper, the Cuntan Yangze Bridge located in a mountainous region is taken as the background to study the effect of different power spectrums on the buffeting performance. A three-dimensional finite element model is set up on the ANSYS platform. The fitted power spectrum of extreme strong wind is recorded and taken as the sample to analyze the buffeting performance. The results are compared with the specified power spectrum in the time and frequency domains. Different from existing studies, buffeting performances with the fitted power spectrum are larger than those with the specified power spectrum on the whole. Two kinds of power spectrum are coincidental in the overall tendency in the frequency domain and are distinct in the low frequency region. Structure performance of long-span suspension bridge in the mountainous region should be the subject of specially paid attention.

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Section: Calculation Of Buffeting Forcementioning

confidence: 99%

Buffeting performance of long-span suspension bridge based on measured wind data in a mountainous region

Yang¹,

Yao²,

Wei³

et al. 2018

J. vibroeng.

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show abstract

“…Therefore, the recorded strong wind at the bridge site is skewed to the bridge axis, and the wind yaw angle θ is 30°. The buffeting response under skew wind can be calculated according to the literature (Scanlan, 1993; Seo and Caracoglia, 2012)…”

Section: Buffeting Responsementioning

confidence: 99%

Buffeting response of a composite cable-stayed bridge in a trumpet-shaped mountain pass

Shen

Gao

et al. 2019

Advances in Structural Engineering

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Previous research showed that wind characteristics were influenced by terrain. To accurately calculate the wind-induced bridge response, this article presented a comprehensive investigation of the wind characteristics of a trumpet-shaped mountain pass by long-term monitoring. Basic strong wind characteristics such as the wind rose, turbulence intensities, turbulence length scales, turbulence spectra and normalized cross-spectrum were discussed using 10 min intervals. Due to the different types of terrain on the two sides of the bridge site, this article attempted to reflect the influence of the terrain on the wind characteristics in different wind directions. The scatter plots of wind characteristics were presented directly on the terrain map. The effects of the turbulence characteristics, mean wind speed and aerodynamic admittance function on buffeting response of the composite cable-stayed bridge were discussed by the multimode coupled frequency domain. The results show that the wind profile is extremely twisted. The larger turbulent integral scale and the lower turbulence intensity appear in the direction along the river. The effect of the mean wind speed on the buffeting response is greater than that of the fluctuating wind characteristics. The aerodynamic admittance function proposed by Holmes has the largest reduction in buffeting response.

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“…These approaches ensure that a building, for example subjected to di erent wind hazard levels (as opposed to the largest event), can achieve a selected performance objective level [41] by taking into account various sources of uncertainty, either in the load or the structure. Recent studies at Northeastern University [42,43,24] have suggested, as an important source of uncertainty, the variability in the measurements of aerodynamic parameters related to wind loads. Consequently, a number of methodologies have been proposed to evaluate structural fragility curves, which are employed to characterize the vulnerability of tall buildings (and long-span bridges) to extreme wind loads.…”

Section: Application Of Uncertainty Analysis To Examine Hurricane Winmentioning

confidence: 99%

“…Poisson process [2,43,44], which is still employed by several researchers in wind engineering [17,29]. However, Vickery's model in [3] employed the Negative Binomial distribution to describe the annual hurricane frequency.…”

Section: Hurricane Genesis Model: Poisson Vs Negative Binomialmentioning

confidence: 99%

Exploring hurricane wind speed along US Atlantic coast in warming climate and effects on predictions of structural damage and intervention costs

Cui

Caracoglia

2016

Engineering Structures

Self Cite

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Hurricanes are among the most destructive weather phenomena, which can cause severe damage to structures and loss of human lives due to strong winds and surge e ects. In 2005, Hurricane Katrina caused 1833 fatalities and $108 billion property damage in several coastal regions along the Gulf of Mexico. In 2012, Hurricane Sandy led to about $65 billion damage in the northeastern coastal region of the USA and in the Ontario Province of Canada. The severity of the consequences, associates with hurricane activity, has motivated several investigations, attempting to forecast hurricane activity in both near and distant future. A short review of the most important models, currently employed for hurricane simulation in wind engineering, is provided in the following section. For example, Georgiou derived a pioneering method to numerically model the hurricane wind eld and to predict wind speed in a hurricane along the US Atlantic coast [1]. Simiu proposed a "single-site" probabilistic model, which employs Monte Carlo simulation to compute hurricane reference wind speeds as a function of ve random quantities or parameters [2]. Subsequently, Vickery proposed an empirical track model [3, 4], derived from statistical linear regression of a large hurricane database. This model has been comprehensively validated and widely used for long-term hurricane wind eld predictions. The initial model by Vickery has been upgraded a number of times to advance the prediction of hurricane wind speeds [5, 6, 7, 8] and it is currently employed in its latest form to generate the wind speed maps in the ASCE-7 civil engineering design standard [9].

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Statistical buffeting response of flexible bridges influenced by errors in aeroelastic loading estimation

Cited by 41 publications

References 22 publications

Buffeting performance of long-span suspension bridge based on measured wind data in a mountainous region

Buffeting performance of long-span suspension bridge based on measured wind data in a mountainous region

Buffeting response of a composite cable-stayed bridge in a trumpet-shaped mountain pass

Exploring hurricane wind speed along US Atlantic coast in warming climate and effects on predictions of structural damage and intervention costs

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