AI tools for human reliability analysis

Johnson, Karl and Morais, Caroline and Patelli, Edoardo; Papadrakakis, M. and Papadopoulos, V. and Stefanou, G., eds. (2023) AI tools for human reliability analysis. In: UNCECOMP 2023: 5th International Conference on Uncertainty Quantification in Computational Science and Engineering. National Technical University of Athens, GRC, pp. 437-452. ISBN 9786185827021

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Abstract

Understanding and quantify human performance is an essential component to guarantee and control the safety of critical installations where human intervention can represent the ultimate safety defence. Human reliability analysis is a time consuming and tedious task usually performed by a human factor expert and therefore subjected to error and variability. In addition, within human reliability analysis there are numerous opportunities to learn from data. However, how data are gathered, presented, shared, and used is an area of continuous development and discussion. In this work, we present a collection of artificial intelligence (AI) tools and methodologies developed to tackle different challenges within the field of human reliability. The aim is to automatise the process, learn from data and support the task of human reliability experts. The collection of tools includes: a tool to automatically classify human errors from accident reports and construct a Bayesian/Credal Networks. The developed works are freely available as part of the open source COSSAN software.

ORCID iDs

Johnson, Karl, Morais, Caroline and Patelli, Edoardo ORCID logoORCID: https://orcid.org/0000-0002-5007-7247; Papadrakakis, M., Papadopoulos, V. and Stefanou, G.
CORE (COnnecting REpositories)