Browsing by Author "Goliger, A. M."

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    Development of an updated fundamental basic wind speed map for SANS 10160-3
    (South African Institution of Civil Engineering, 2017-11) Kruger, A. C.; Retief, J. V.; Goliger, A. M.
    ENGLISH ABSTRACT: This paper evaluates the need for updating the strong wind climate stipulations of South Africa for the design of structures in accordance with SANS 10160-3:2010, as based on the latest information presented by Kruger et al (2013a; 2013b). The primary objective is to provide the geographic distribution of the characteristic gust wind speed by means of the fundamental value of the basic wind speed, stipulated as vb,0in SANS 10160-3. A reassessment of previously published information is made to incorporate additional wind speed modelling results and to investigate identified anomalies. The format of presentation, based on local municipal districts, is subsequently motivated, assessed and implemented. In order to provide for situations requiring the consideration of the dynamic effects of wind loading, similar information on characteristic hourly mean wind speed is provided. It is concluded that the presentation of wind speed on a district basis provides an effective balance between the spatial resolution of the available information and its use in operational standardised design.
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    Strong winds in South Africa : part 1 : application of estimation methods
    (South African Institution of Civil Engineering, 2013-08) Kruger, A. C.; Retief, J. V.; Goliger, A. M.
    The accurate estimation of strong winds is of cardinal importance to the built environment, particularly in South Africa, where wind loading represents the dominant environmental action to be considered in the design of structures. While the Gumbel method remains the most popular applied method to estimate strong wind quantiles, several factors should influence the consideration of alternative approaches. In South Africa, the most important factors influencing the choice of method are the mixed strong wind climate and the lengths of available wind measurement records. In addition, the time-scale of the estimations (in this case one hour and 2–3 seconds) influences the suitability of some methods. The strong wind climate is dominated by synoptic scale disturbances along the coast and adjacent interior, and mesoscale systems, i.e. thunderstorms, in the biggest part of the interior. However, in a large part of South Africa more than one mechanism plays a significant role in the development of strong winds. For these regions the application of a mixed-climate approach is recommended as more appropriate than the Gumbel method. In South Africa, reliable wind records are in most cases shorter than 20 years, which makes the application of a method developed for short time series advisable. In addition it is also recommended that the shape parameter be set to zero, which translates to the Gumbel method when only annual maxima are employed. In the case of the Peak-Over-Threshold (POT) method, one of several methods developed for short time series, the application of the Exponential Distribution instead of the Generalised Pareto Distribution is recommended. However, the POT method is not suitable for estimations over longer time scales, e.g. one hour averaging, due to the high volumes of dependent strong wind values in the data sets to be utilised. The results of an updated assessment, or the present strong wind records reported in this paper, serve as input to revised strong wind maps, as presented in the accompanying paper (see page 46).
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    Strong winds in South Africa : part 2 : mapping of updated statistics
    (South African Institution of Civil Engineering, 2013-08) Kruger, A. C.; Retief, J. V.; Goliger, A. M.
    Although wind is the most important environmental action on buildings and structures in South Africa, the last comprehensive strong wind analysis was conducted in 1985. The current wind loading code is still based on the strong wind quantiles forthcoming from that analysis. Wind data available for strong wind analysis has increased about five-fold, due to the employment of automatic weather station (AWS) technology by the South African Weather Service. This makes an updated assessment of strong winds in South Africa imperative. Based on the estimation of strong winds as reported in the accompanying paper (see page 29 in this volume), the spatial interpolation of 50-year characteristic strong wind values to provide updated design wind speed maps is reported in this paper. In addition to taking account of short recording periods and the effects of the mixed strong wind climate, the exposure of the weather stations was considered and correction factors applied. Quantile values were adjusted to compensate for the small data samples. The resultant design maps reveal regions of relatively high and low quantiles, but with an improved relationship with physical conditions compared to the previous analyses. Consequently some significant differences in quantiles between the present and previous analyses were found. The complexity of the resulting strong wind maps is not only the result of the improved resolution of the larger number of weather stations, but also due to an improved identification of the effects of physical factors such as the mixed strong wind climate and topography. Guidance can also be derived for future updating, such as incorporating accumulated observations and improved coverage by additional AWS in critical regions.