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• W2012846510 abstract "Abstract In the companion ATC 2014 paper by Morrison and Spencer, the quantile regression data processing technique was introduced. In the current paper, we use the technique to estimate the local pressure trend at various quantiles, namely 90%, 99% and 99.9% and to revise the recommendations in ISO 19906 (ISO International Standard. Petroleum and natural gas industries—Arctic offshore structures). In addition we make recommendations on the form and parameters of the random pressure distribution for use in Monte-Carlo methods. The quantile regression method is also used to analyse global pressure data for wide structures for both the Baltic ice measurements and the Arctic ice measurements and compare with the expressions provided in ISO. The alternative statistical analysis provided in this paper indicates that changes to the ISO formulations are required. Introduction In the companion paper (Morrison and Spencer, 2014) the quantile regression method of analysing data was introduced. The example data set was the local pressure for ice crushing failure (Masterson et al. 2007), who used least-squares regression along with binning of the data in deriving the design pressure guidelines. This result has been incorporated into an offshore structure standard (ISO, 2010). Using this data set it was shown (Morrison and Spencer, 2014) that a fuller picture of the data could be obtained using a method that does not require binning or grouping of the data. As was also demonstrated, the power-law fit parameters determined using the quantile regression are different from the recommendations contained within ISO (2010). In this paper we use the quantile regression approach to estimate what would be an appropriate probability distribution function for the ice crushing pressure at local scale and to produce new recommendations for local design pressure at a range of quantile, or equivalently a range of probabilities of exceedence. In addition we use the quantile regression method to analyse the data used in generating the Arctic global and the Baltic global pressure recommendations given in ISO (2010) and to compare with the ISO recommendations." @default.
• W2012846510 created "2016-06-24" @default.
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• W2012846510 date "2014-02-10" @default.
• W2012846510 modified "2023-09-25" @default.
• W2012846510 title "Quantile Regression as a Tool for Investigating Local and Global Ice Pressures" @default.
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• W2012846510 doi "https://doi.org/10.4043/24550-ms" @default.
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