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

Raphael Moura, Michael Beer, Edoardo Patelli, John Lewis

Research output: Contribution to journalArticle

13 Citations (Scopus)
1 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)58-70
Number of pages13
JournalSafety Science
Volume99
Issue numberPart A
Early online date18 Mar 2017
DOIs
Publication statusPublished - 30 Nov 2017

Keywords

  • accident analysis
  • human factors
  • learning from accidents
  • MATA-D
  • self-organising maps

Fingerprint Dive into the research topics of 'Learning from major accidents: graphical representation and analysis of multi-attribute events to enhance risk communication'. Together they form a unique fingerprint.

  • Cite this