Details
| Original language | English |
|---|---|
| Number of pages | 8 |
| Publication status | Published - 2015 |
| Externally published | Yes |
| Event | 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 - Vancouver, Canada Duration: 12 Jul 2015 → 15 Jul 2015 |
Conference
| Conference | 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 |
|---|---|
| Country/Territory | Canada |
| City | Vancouver |
| Period | 12 Jul 2015 → 15 Jul 2015 |
Abstract
Regardless of the evolution of engineering systems and fabrication methods, recent major accidents exposed the risk behind modern human economic activities to an inquiring and perplexed society. These events brought out the fact that interactions between complex systems, cutting-edge technologies and human factors may trigger particular accident sequences that are very difficult to predict and mitigate through traditional risk assessment tools. Thus, the purpose of this study is to overcome barriers to dealing with complex data by translating multi-attribute events into a two-dimensional visualisation framework, providing means to communicate high-technology risks and to disclose surrounding factors and tendencies that could lead to the manifestation of human errors. This paper first discusses the human error and human factors role in industrial accidents. The second part applies Kohonen's self-organising maps neural network theory to an accident dataset developed by the authors, as an attempt to improve data exploration and classify information from past events. Graphical interfaces are then generated to produce further insight into the conditions leading to the human errors genesis and to facilitate risk communication among stakeholders.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Mathematics(all)
- Statistics and Probability
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
2015. Paper presented at 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012, Vancouver, Canada.
Research output: Contribution to conference › Paper › Research › peer review
}
TY - CONF
T1 - Learning from accidents
T2 - 12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012
AU - Moura, Raphael
AU - Beer, Michael
AU - Lewis, John
AU - Patelli, Edoardo
N1 - Funding Information: The authors gratefully acknowledge the insights from Dr. Franz Knoll (NCK Inc.). This study was partially funded by CAPES (Proc. no 5959/13-6).
PY - 2015
Y1 - 2015
N2 - Regardless of the evolution of engineering systems and fabrication methods, recent major accidents exposed the risk behind modern human economic activities to an inquiring and perplexed society. These events brought out the fact that interactions between complex systems, cutting-edge technologies and human factors may trigger particular accident sequences that are very difficult to predict and mitigate through traditional risk assessment tools. Thus, the purpose of this study is to overcome barriers to dealing with complex data by translating multi-attribute events into a two-dimensional visualisation framework, providing means to communicate high-technology risks and to disclose surrounding factors and tendencies that could lead to the manifestation of human errors. This paper first discusses the human error and human factors role in industrial accidents. The second part applies Kohonen's self-organising maps neural network theory to an accident dataset developed by the authors, as an attempt to improve data exploration and classify information from past events. Graphical interfaces are then generated to produce further insight into the conditions leading to the human errors genesis and to facilitate risk communication among stakeholders.
AB - Regardless of the evolution of engineering systems and fabrication methods, recent major accidents exposed the risk behind modern human economic activities to an inquiring and perplexed society. These events brought out the fact that interactions between complex systems, cutting-edge technologies and human factors may trigger particular accident sequences that are very difficult to predict and mitigate through traditional risk assessment tools. Thus, the purpose of this study is to overcome barriers to dealing with complex data by translating multi-attribute events into a two-dimensional visualisation framework, providing means to communicate high-technology risks and to disclose surrounding factors and tendencies that could lead to the manifestation of human errors. This paper first discusses the human error and human factors role in industrial accidents. The second part applies Kohonen's self-organising maps neural network theory to an accident dataset developed by the authors, as an attempt to improve data exploration and classify information from past events. Graphical interfaces are then generated to produce further insight into the conditions leading to the human errors genesis and to facilitate risk communication among stakeholders.
UR - http://www.scopus.com/inward/record.url?scp=84978732432&partnerID=8YFLogxK
U2 - 10.14288/1.0076074
DO - 10.14288/1.0076074
M3 - Paper
Y2 - 12 July 2015 through 15 July 2015
ER -