Peak Non-Gaussian Wind Effects for Database-Assisted Low-Rise Building Design

Abstract

Current procedures for estimating the peaks of the stochastic response of tall buildings to wind are based on the assumption that the response is Gaussian. Those procedures are therefore inapplicable to low-rise buildings, in which time histories of wind-induced internal forces are generally non-Gaussian. In this paper, an automated procedure is developed for obtaining from such time histories sample statistics of internal force peaks for low-rise building design and codification. The procedure is designed for use in software for calculating internal force time series by the database-assisted design approach. A preliminary step in the development of the procedure is the identification of the appropriate marginal probability distribution of the time series using the probability plot correlation coefficient method. The result obtained is that the gamma distribution and a normal distribution are appropriate for estimating the peaks corresponding, respectively, to the longer and shorter tail of the time series' histograms. The distribution of the peaks is then estimated by using the standard translation processes approach. It is found that the peak distribution can be represented by the Extreme Value Type I ͑Gumbel͒ distribution. Because estimates obtained from this approach are based on the entire information contained in the time series, they are more stable than estimates based on observed peaks. The procedure can be used to establish minimum acceptable requirements with respect to the duration and sampling rate of the time series of interest, so that the software used for database-assisted design will be both efficient and accurate.

Field measurements of extreme pressures on a flat roof of a low-rise building during typhoons

“…Extreme value analysis was performed using an automated procedure developed by Sadek and Simiu (2002) to obtain the distribution of peak pressure from a single sample record for removing the uncertainties inherent in the randomness of the peaks. In this study, comparisons are presented between the estimated pressures and those derived from ASCE 7-10 Standard (American Society of Civil Engineers, 2010).…”

Derivation of equivalent gust effect factors for wind loading on low-rise buildings through Database-Assisted-Design approach

“…(2) was directly estimated by DAD from the maximum peak value extracted from the wind tunnel records. Nevertheless, due to the probabilistic nature of EGEF, it was decided to independently assess G e , record by record, in accordance with the procedure developed by Simiu and Sadek [16].…”

“…The corresponding peak in the nonGaussian space, m Loc1 , was then estimated from the probability Fig. 3(b) [16]. The quantity ϕ is the probability density function of the Gaussian variable.…”

“…(a) Mapping procedure for peak bending moment estimation from nonGaussian marginal process and (b) corresponding point on the marginal distribution curves[16].…”

A one- and two-dimensional generalized Pareto model for a river flow

“…See, for instance [1][2][3]. In most cases, a 0307-904X/$ -see front matter Ó 2005 Elsevier Inc. All rights reserved.…”