Details
| Original language | English |
|---|---|
| Pages (from-to) | 37-45 |
| Number of pages | 9 |
| Journal | Safety Science |
| Volume | 84 |
| Early online date | 14 Dec 2015 |
| Publication status | Published - Apr 2016 |
| Externally published | Yes |
Abstract
Despite the massive developments in new technologies, materials and industrial systems, notably supported by advanced structural and risk control assessments, recent major accidents are challenging the practicality and effectiveness of risk control measures designed to improve reliability and reduce the likelihood of losses. Contemporary investigations of accidents occurred in high-technology systems highlighted the connection between human-related issues and major events, which led to catastrophic consequences. Consequently, the understanding of human behavioural characteristics interlaced with current technology aspects and organisational context seems to be of paramount importance for the safety & reliability field. First, significant drawbacks related to the human performance data collection will be minimised by the development of a novel industrial accidents dataset, the Multi-attribute Technological Accidents Dataset (MATA-D), which groups 238 major accidents from different industrial backgrounds and classifies them under a common framework (the Contextual Control Model used as basis for the Cognitive Reliability and Error Analysis Method). The accidents collection and the detailed interpretation will provide a rich data source, enabling the usage of integrated information to generate input to design improvement schemes. Then, implications to improve robustness of system design and tackle the surrounding factors and tendencies that could lead to the manifestation of human errors will be effectively addressed.
Keywords
- Accident analysis, CREAM, Human factors, Human reliability analysis, MATA-D, Risk & safety in design
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
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Safety Science, Vol. 84, 04.2016, p. 37-45.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning from major accidents to improve system design
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 – Brazil (Proc. n° 5959/13-6).
PY - 2016/4
Y1 - 2016/4
N2 - Despite the massive developments in new technologies, materials and industrial systems, notably supported by advanced structural and risk control assessments, recent major accidents are challenging the practicality and effectiveness of risk control measures designed to improve reliability and reduce the likelihood of losses. Contemporary investigations of accidents occurred in high-technology systems highlighted the connection between human-related issues and major events, which led to catastrophic consequences. Consequently, the understanding of human behavioural characteristics interlaced with current technology aspects and organisational context seems to be of paramount importance for the safety & reliability field. First, significant drawbacks related to the human performance data collection will be minimised by the development of a novel industrial accidents dataset, the Multi-attribute Technological Accidents Dataset (MATA-D), which groups 238 major accidents from different industrial backgrounds and classifies them under a common framework (the Contextual Control Model used as basis for the Cognitive Reliability and Error Analysis Method). The accidents collection and the detailed interpretation will provide a rich data source, enabling the usage of integrated information to generate input to design improvement schemes. Then, implications to improve robustness of system design and tackle the surrounding factors and tendencies that could lead to the manifestation of human errors will be effectively addressed.
AB - Despite the massive developments in new technologies, materials and industrial systems, notably supported by advanced structural and risk control assessments, recent major accidents are challenging the practicality and effectiveness of risk control measures designed to improve reliability and reduce the likelihood of losses. Contemporary investigations of accidents occurred in high-technology systems highlighted the connection between human-related issues and major events, which led to catastrophic consequences. Consequently, the understanding of human behavioural characteristics interlaced with current technology aspects and organisational context seems to be of paramount importance for the safety & reliability field. First, significant drawbacks related to the human performance data collection will be minimised by the development of a novel industrial accidents dataset, the Multi-attribute Technological Accidents Dataset (MATA-D), which groups 238 major accidents from different industrial backgrounds and classifies them under a common framework (the Contextual Control Model used as basis for the Cognitive Reliability and Error Analysis Method). The accidents collection and the detailed interpretation will provide a rich data source, enabling the usage of integrated information to generate input to design improvement schemes. Then, implications to improve robustness of system design and tackle the surrounding factors and tendencies that could lead to the manifestation of human errors will be effectively addressed.
KW - Accident analysis
KW - CREAM
KW - Human factors
KW - Human reliability analysis
KW - MATA-D
KW - Risk & safety in design
UR - http://www.scopus.com/inward/record.url?scp=84949637860&partnerID=8YFLogxK
U2 - 10.1016/j.ssci.2015.11.022
DO - 10.1016/j.ssci.2015.11.022
M3 - Article
AN - SCOPUS:84949637860
VL - 84
SP - 37
EP - 45
JO - Safety Science
JF - Safety Science
SN - 0925-7535
ER -