EPJ Nuclear Sci. Technol.
Volume 3, 2017
|Number of page(s)
|10 July 2017
Probabilistic risk bounds for the characterization of radiological contamination
1 EDF Lab Les Renardières, Materials and Mechanics of Components Department, 77818 Moret-sur-Loing, France
2 EDF Lab Chatou, Department of Performance, Industrial Risk, Monitoring for Maintenance and Operations, 78401 Chatou, France
3 CEA Nuclear Energy Division, Centre de Cadarache, 13108 Saint-Paul-lès-Durance, France
⁎ e-mail: firstname.lastname@example.org
Received in final form: 26 May 2017
Accepted: 19 June 2017
Published online: 10 July 2017
The radiological characterization of contaminated elements (walls, grounds, objects) from nuclear facilities often suffers from too few measurements. In order to determine risk prediction bounds on the level of contamination, some classic statistical methods may therefore be unsuitable, as they rely upon strong assumptions (e.g., that the underlying distribution is Gaussian) which cannot be verified. Considering that a set of measurements or their average value come from a Gaussian distribution can sometimes lead to erroneous conclusions, possibly not sufficiently conservative. This paper presents several alternative statistical approaches which are based on much weaker hypotheses than the Gaussian one, which result from general probabilistic inequalities and order-statistic based formulas. Given a data sample, these inequalities make it possible to derive prediction intervals for a random variable which can be directly interpreted as probabilistic risk bounds. For the sake of validation, they are first applied to simulated data generated from several known theoretical distributions. Then, the proposed methods are applied to two data sets obtained from real radiological contamination measurements.
© G. Blatman et al., published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.