This paper discusses the uncertainty when estimating extreme values of wind-induced lateral accelerations in a high-rise building based on wind-tunnel measurements. The acceleration signals for an aeroelastic scale model under ten different velocities and three different wind angles were processed to evaluate the extreme acceleration values. The empirical cumulative distribution function (CDF) and probability density function (PDF) trends of the peaks were estimated and compared with the analytical models, which showed satisfactory fits. An effort was made for the best fit for the empirical CDF through the numerical expansion of the peak set using Polynomial Chaos Expansion (PCE). It was confirmed that in this case, the lack of a reliable fit was not due to the number of peaks. In addition, analytical models of the Gaussian and non-Gaussian processes were applied to estimate the extreme values using the entire process and the sub-processes, and this paper compares and discusses the results. Finally, the variability of the extreme acceleration values estimated using a total of ten different methods is discussed.

Peak value estimation for wind-induced lateral accelerations in a high-rise building

Fabio Rizzo
;
2021-01-01

Abstract

This paper discusses the uncertainty when estimating extreme values of wind-induced lateral accelerations in a high-rise building based on wind-tunnel measurements. The acceleration signals for an aeroelastic scale model under ten different velocities and three different wind angles were processed to evaluate the extreme acceleration values. The empirical cumulative distribution function (CDF) and probability density function (PDF) trends of the peaks were estimated and compared with the analytical models, which showed satisfactory fits. An effort was made for the best fit for the empirical CDF through the numerical expansion of the peak set using Polynomial Chaos Expansion (PCE). It was confirmed that in this case, the lack of a reliable fit was not due to the number of peaks. In addition, analytical models of the Gaussian and non-Gaussian processes were applied to estimate the extreme values using the entire process and the sub-processes, and this paper compares and discusses the results. Finally, the variability of the extreme acceleration values estimated using a total of ten different methods is discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/246765
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