Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication

Moura, Raphael ORCID: 0000-0003-3494-5945, Beer, Michael ORCID: 0000-0002-0611-0345, Patelli, Edoardo ORCID: 0000-0002-5007-7247 and Lewis, John (2017) Learning from major accidents: Graphical representation and analysis of multi-attribute events to enhance risk communication. SAFETY SCIENCE, 99. pp. 58-70. ISSN 0925-7535, 1879-1042

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Abstract

Major accidents are complex, multi-attribute events, originated from the interactions between intricate systems, cutting-edge technologies and human factors. Usually, these interactions trigger very particular accident sequences, which are hard to predict but capable of producing exacerbated societal reactions and impair communication channels among stakeholders. Thus, the purpose of this work is to convert high-dimensional accident data into a convenient graphical alternative, in order to overcome barriers to communicate risk and enable stakeholders to fully understand and learn from major accidents. This paper first discusses contemporary views and biases related to human errors in major accidents. The second part applies an artificial neural network approach to a major accident dataset, to disclose common patterns and significant features. The complex data will be then translated into 2-D maps, generating graphical interfaces which will produce further insight into the conditions leading to accidents and support a novel and comprehensive “learning from accidents” experience.

Item Type:	Article
Uncontrolled Keywords:	Accident analysis, Learning from accidents, Human factors, MATA-D, Self-organising maps
Depositing User:	Symplectic Admin
Date Deposited:	21 Mar 2017 07:29
Last Modified:	07 Dec 2024 01:56
DOI:	10.1016/j.ssci.2017.03.005
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3006534