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One of the key elements for practical implementation of RBD in geotechnical engineering is the characterization of calculation model uncertainty. The calculation models used in the analysis and design of geotechnical structures are in general incomplete and inexact as a result of lack of knowledge or simplification for mathematical convenience. The objective of this chapter is to present available methodologies for characterizing calculation model uncertainty for geotechnical structures. Although the examples given are limited, the methodologies presented are applicable to a wide range of geotechnical structures. First, the general statistical characterization entailing (a) exploratory data analysis, (b) outliers detection and correction of anomalous values, (c) using the corrected data to compute the sample moments (mean, standard deviation, skewness, and kurtosis), (d) verification of the randomness of the model factor, (e) determining the appropriate probability distribution for M and (f) removal of statistical dependencies is presented. This is followed by a presentation of published model factor statistics for geotechnical structures at ultimate and serviceability limit states. It is concluded that significant data on model uncertainty statistics is now available as prior information to enable reliability calibration of partial factors required for semi-probabilistic design approach. © 2017 by Apple Academic Press, Inc.

ISO2394:2015 contains a new informative Annex D on "Reliability of Geotechnical Structures". The emphasis in Annex D is to identify and characterize critical elements of the geotechnical reliability-based design (RBD) process, while respecting the diversity of geotechnical engineering practice arising from the influence of site-specific conditions. Annex D should pave the way for future code revisions to inject greater realism into geotechnical RBD. The most important element is the characterization of geotechnical variability. There are some noteworthy features: (1) the coefficient of variation (COV) of a geotechnical design parameter does not fall within a narrow range of values, (2) the multivariate nature of geotechnical data can be exploited to reduce the COV, (3) spatial variability affects the limit state beyond reduction in COV due to spatial averaging, and (4) statistical uncertainty may be significant, because the volume of soil sampled is a minute fraction of the volume of interest. Another important element is the characterization of model uncertainty. Geotechnical design codes, be it reliability-based or otherwise, must cater to diverse local site conditions and diverse local practices that grew and evolved over the years to suit these conditions. One obvious example is that the COVs of geotechnical parameters can vary over a wide range, because diverse property evaluation methodologies exist to cater to these diverse practice and site conditions. Another example is that deep foundations are typically installed in layered soil profiles that vary from site to site. These diverse site specific design scenarios do not surface in structural engineering. Section D.5.2 notes that reliability calibration for geotechnical RBD is more challenging for this reason. © 2016 Elsevier Ltd.

Accurate extreme wind statistics are important for the design of a safe and economic built environment. The recent revision of the South African Wind Loading Code for engineers (SANS 10160-3:2011) will also include a reassessment of design wind speed statistics. In addition, the Wind Atlas of South Africa (WASA project) focuses, amongst others, on the development of a Regional Extreme Wind Climate (REWC) for South Africa. Wind farms are planned for areas with relatively strong and sustained winds, with wind turbines classed according to their suitability for different wind conditions. The REWC statistics are used during the construction and design phase to make assumptions about the local strong wind climate that the wind turbines will be exposed to, with the local environment and topography as additional input. The simultaneous development of the REWC and revision of the extreme wind statistics of South Africa created an opportunity to bring together a range of expertise that could contribute to the optimal development of design wind speed information. These include the knowledge of the statistical extraction of extreme wind observations from reanalysis and model data, the quality control and extreme value analysis of measured wind data, the reliability basis of statistical results, and the principles of wind action on structures and its standardization.

The complexity of the wind climate of Cape Town and its surroundings can be shown by the measurements of specific wind phenomena by weather stations around Table Mountain. It is shown that there are substantial differences between wind speed characteristics affecting various parts of the city. These differences between the wind at the different locations are further complicated by the main strong wind mechanisms prevailing in the region, i.e. north-westerly winds from passing extratropical cyclones, mainly in the austral winter, and south-easterlies from ridging high-pressure systems, mainly in the summer months. This initial investigation is the precursor of a broader study involving wind tunnel modelling, wind measurements and climate modelling, to provide a comprehensive analysis of the spatial variability of strong winds in the Cape Peninsula.