Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Safety and Reliability of Complex Engineered Systems |
| Untertitel | Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015 |
| Herausgeber/-innen | Luca Podofillini, Bruno Sudret, Božidar Stojadinović, Enrico Zio, Wolfgang Kröger |
| Seiten | 3049-3056 |
| Seitenumfang | 8 |
| Publikationsstatus | Veröffentlicht - 2015 |
| Extern publiziert | Ja |
| Veranstaltung | 25th European Safety and Reliability Conference, ESREL 2015 - Zurich, Swasiland Dauer: 7 Sept. 2015 → 10 Sept. 2015 |
Abstract
High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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Safety and Reliability of Complex Engineered Systems: Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015. Hrsg. / Luca Podofillini; Bruno Sudret; Božidar Stojadinović; Enrico Zio; Wolfgang Kröger. 2015. S. 3049-3056.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Learning from accidents
T2 - 25th European Safety and Reliability Conference, ESREL 2015
AU - Moura, R.
AU - Beer, M.
AU - Patelli, E.
AU - Lewis, J.
AU - Knoll, F.
PY - 2015
Y1 - 2015
N2 - High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed.
AB - High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed.
UR - http://www.scopus.com/inward/record.url?scp=84959010852&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84959010852
SN - 9781138028791
SP - 3049
EP - 3056
BT - Safety and Reliability of Complex Engineered Systems
A2 - Podofillini, Luca
A2 - Sudret, Bruno
A2 - Stojadinović, Božidar
A2 - Zio, Enrico
A2 - Kröger, Wolfgang
Y2 - 7 September 2015 through 10 September 2015
ER -