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By M. A. Hicks

This e-book provides leading edge thoughts for characterising, quantifying and modelling geomaterial variability as well as tools for quantifying the impact of this variability at the functionality of geotechnical constructions. It comprises state of the art refereed magazine papers by means of prime overseas researchers in addition to written and casual discussions on a variety of key submissions that have been provided at a Symposium on the establishment of Civil Engineers on ninth could 2005

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Extra resources for Risk and variability in geotechnical engineering

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The spatial averages should be determined by test methods where a relatively large part of the column cross-section is tested. A method for assessment of the correlation structure between spatial averages is presented by Vanmarcke (1977). The correlation structures for improved soil properties at full scale will facilitate the determination of a proper selection of safety factors. Analyses of variance Statistical analyses of variance (ANOVA) were performed in order to investigate whether the retrieval rate, the number of blades on the mixing tool, the type of mixing tool, the rotational speed, the air pressure in the storage tank, or the diameter of the outlet hole of the mixing tool have an ON HORIZONTAL VARIABILITY IN LIME-CEMENT COLUMNS Semi-variogram value, ã(h): mm2 600 not recur in the majority of tests.

On Ground Improvement Geosystems, Tokyo 2, 913–918. Cernica, J. N. (1995). Geotechnical engineering: Soil mechanics. New York: Wiley. Danckwerts, P. V. (1953). Theory of mixtures and mixing. Research: Science and its Application in Industry 6, 355–361. Deutsch, C. V. (2002). Geostatistical reservoir modeling. New York: Oxford University Press. EuroSoilStab (2002). Development of design and construction methods to stabilise soft organic soils: Design guide soft soil stabilisation, CT97–0351, Project No.

Reliability analysis conducted using lumped variables, by ignoring autocorrelation, generally leads to biased results. However, the amount of geotechnical data available is often limited, making the determination of the autocorrelation functions difficult. Lack of sufficient statistical information causes additional uncertainty about the autocorrelation function itself. By using the bootstrap method, levels of uncertainty associated with autocorrelation functions and autocorrelation distances could be estimated, and included in reliability analyses.

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