Details
| Original language | English |
|---|---|
| Pages (from-to) | 196-214 |
| Number of pages | 19 |
| Journal | Safety Science |
| Volume | 99 |
| Issue number | B |
| Early online date | 11 May 2017 |
| Publication status | Published - Nov 2017 |
Abstract
Many industries are subjected to major hazards, which are of great concern to stakeholders groups. Accordingly, efforts to control these hazards and manage risks are increasingly made, supported by improved computational capabilities and the application of sophisticated safety and reliability models. Recent events, however, have revealed that apparently rare or seemingly unforeseen scenarios, involving complex interactions between human factors, technologies and organisations, are capable of triggering major catastrophes. The purpose of this work is to enhance stakeholders’ trust in risk management by developing a framework to verify if tendencies and patterns observed in major accidents were appropriately contemplated by risk studies. This paper first discusses the main accident theories underpinning major catastrophes. Then, an accident dataset containing contributing factors from major events occurred in high-technology industrial domains serves as basis for the application of a clustering and data mining technique (self-organising maps – SOM), allowing the exploration of accident information gathered from in-depth investigations. Results enabled the disclosure of common patterns in major accidents, leading to the development of an attribute list to validate risk assessment studies to ensure that the influence of human factors, technological issues and organisational aspects was properly taken into account.
Keywords
- Human factors, Learning from accidents, MATA-D, Organisations, Risk studies validation, Self-organising maps
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
- Medicine(all)
- Public Health, Environmental and Occupational Health
Sustainable Development Goals
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In: Safety Science, Vol. 99, No. B, 11.2017, p. 196-214.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning from accidents
T2 - Interactions between human factors, technology and organisations as a central element to validate risk studies
AU - Moura, Raphael
AU - Beer, Michael
AU - Patelli, Edoardo
AU - Lewis, John
AU - Knoll, Franz
N1 - Funding information: This study was partially funded by CAPES [Grant n° 5959/13-6 ].
PY - 2017/11
Y1 - 2017/11
N2 - Many industries are subjected to major hazards, which are of great concern to stakeholders groups. Accordingly, efforts to control these hazards and manage risks are increasingly made, supported by improved computational capabilities and the application of sophisticated safety and reliability models. Recent events, however, have revealed that apparently rare or seemingly unforeseen scenarios, involving complex interactions between human factors, technologies and organisations, are capable of triggering major catastrophes. The purpose of this work is to enhance stakeholders’ trust in risk management by developing a framework to verify if tendencies and patterns observed in major accidents were appropriately contemplated by risk studies. This paper first discusses the main accident theories underpinning major catastrophes. Then, an accident dataset containing contributing factors from major events occurred in high-technology industrial domains serves as basis for the application of a clustering and data mining technique (self-organising maps – SOM), allowing the exploration of accident information gathered from in-depth investigations. Results enabled the disclosure of common patterns in major accidents, leading to the development of an attribute list to validate risk assessment studies to ensure that the influence of human factors, technological issues and organisational aspects was properly taken into account.
AB - Many industries are subjected to major hazards, which are of great concern to stakeholders groups. Accordingly, efforts to control these hazards and manage risks are increasingly made, supported by improved computational capabilities and the application of sophisticated safety and reliability models. Recent events, however, have revealed that apparently rare or seemingly unforeseen scenarios, involving complex interactions between human factors, technologies and organisations, are capable of triggering major catastrophes. The purpose of this work is to enhance stakeholders’ trust in risk management by developing a framework to verify if tendencies and patterns observed in major accidents were appropriately contemplated by risk studies. This paper first discusses the main accident theories underpinning major catastrophes. Then, an accident dataset containing contributing factors from major events occurred in high-technology industrial domains serves as basis for the application of a clustering and data mining technique (self-organising maps – SOM), allowing the exploration of accident information gathered from in-depth investigations. Results enabled the disclosure of common patterns in major accidents, leading to the development of an attribute list to validate risk assessment studies to ensure that the influence of human factors, technological issues and organisational aspects was properly taken into account.
KW - Human factors
KW - Learning from accidents
KW - MATA-D
KW - Organisations
KW - Risk studies validation
KW - Self-organising maps
UR - http://www.scopus.com/inward/record.url?scp=85028743991&partnerID=8YFLogxK
U2 - 10.1016/j.ssci.2017.05.001
DO - 10.1016/j.ssci.2017.05.001
M3 - Article
AN - SCOPUS:85028743991
VL - 99
SP - 196
EP - 214
JO - Safety Science
JF - Safety Science
SN - 0925-7535
IS - B
ER -