A comparison of methods to estimate peak wind loads on buildings

Xinlai Peng, Luping Yang, Eri Gavanski, Kurtis Gurley, David Prevatt

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

The most appropriate statistical technique to estimate a peak pressure coefficient from wind tunnel data is not a settled issue. The lack of a standard acceptable method can lead to inconsistent definitions and interpretations of peak pressure coefficients, particularly since time constraints associated with wind tunnel tests necessitate relatively short test durations. A Gumbel model is commonly used to represent the peak distribution, where parameters are determined using observed peaks. Recent papers have proposed several variations of a peak estimation procedure using the entire time history and a translation from a Gaussian peak distribution model to non-Gaussian. It is shown that, in the case of mildly non-Gaussian data, translation methods achieve accuracy comparable to the Gumbel method. It is also shown that translation methods lose accuracy when the record deviates significantly from Gaussian, while the Gumbel model maintains stable accuracy and precision. This paper presents two new translation-based peak pressure coefficient estimation schemes that offer accurate and stable performance for strongly non-Gaussian data. Very long duration wind tunnel data provide empirical peak distributions with which to compare the relative performance of the Gumbel, existing translation and proposed new translation methods. One of the new methods slightly outperforms the Gumbel method.

Original languageEnglish
Pages (from-to)11-23
Number of pages13
JournalJournal of Wind Engineering and Industrial Aerodynamics
Volume126
DOIs
Publication statusPublished - 2014 Mar 1

Keywords

  • Comparative study
  • Gumbel distribution
  • Low-rise building
  • Non-Gaussian process
  • Peak pressure coefficient estimation
  • Translation process
  • Wind pressure
  • Wind tunnel

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Renewable Energy, Sustainability and the Environment
  • Mechanical Engineering

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