Learning from major accidents
Graphical representation and analysis of multi-attribute events to enhance risk communication
- authored by
- Raphael Moura, Michael Beer, Edoardo Patelli, John Lewis
- 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.
- Organisation(s)
-
Institute for Risk and Reliability
- External Organisation(s)
-
University of Liverpool
Brazilian National Agency for Petroleum, Natural Gas and Biofuels (ANP)
- Type
- Article
- Journal
- Safety Science
- Volume
- 99
- Pages
- 58-70
- No. of pages
- 13
- ISSN
- 0925-7535
- Publication date
- 11.2017
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality, Safety Research, Public Health, Environmental and Occupational Health
- Sustainable Development Goals
- SDG 3 - Good Health and Well-being
- Electronic version(s)
-
https://strathprints.strath.ac.uk/72213/1/Moura_etal_SS_2017_Learning_from_major_accidents_graphical_representation.pdf (Access:
Open)
https://doi.org/10.1016/j.ssci.2017.03.005 (Access: Closed)
