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Abstract

In this paper, we investigate the possibility of embedding neural networks, appropriately trained on the results of a Monte Carlo plant reliability evaluation, within a classical decomposition scheme for efficiently performing multiparametric sensitivity analyses of a reliability model. These analyses are of great importance for the identification of critical systems, structures and components of hazardous plants, such as nuclear or chemical ones, thus providing significant insights for their risk-based design and management.

Additional information

Authors: MARSEGUERRA M, Department of Nuclear Engineering, Polytechnic of Milan (IT);MASINI R, Department of Nuclear Engineering, Polytechnic of Milan (IT);ZIO E, Department of Nuclear Engineering, Polytechnic of Milan (IT);COJAZZI G, JRC-Nuclear Safety Unit, Ispra (IT)
Bibliographic Reference: An oral report given at: SAMO 2001: Third International Symposium on Sensativity Analysis of Model Output. Organised by: CIEMAT. Held in: Madrid Spain, 18-20 June, 2001
Record Number: 200013410 / Last updated on: 2001-06-27
Category: PUBLICATION
Original language: en
Available languages: en
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