Details
| Original language | English |
|---|---|
| Title of host publication | Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures |
| Subtitle of host publication | Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 |
| Pages | 2225-2232 |
| Number of pages | 8 |
| Edition | Boca Raton |
| ISBN (electronic) | 978-1-315-88488-2 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013 - New York, NY, United States Duration: 16 Jun 2013 → 20 Jun 2013 |
Abstract
In order to minimize risks it is of paramount importance to take into account the effects of uncertainties from the design stage. In fact, the knowledge about the behaviour of complex systems and future conditions is always incomplete. Risk-based optimization is a powerful and well-recognized tool for identification of the optimal (robust) design with a systematic consideration of uncertainty. More specifically, this approach looks for the best design solution, whilst minimizing the risk, thus considering the effects of uncertainties giving a measure of safety levels. However, traditional optimization procedures come out with a punctual (single) optimum that rarely can be translated in engineering solutions, leaving little or no room for manufacturing and operating tolerances. The optimization shall be given an even more rational connotation for treating the uncertainties that comprises set-wise quantities. Solution is found by means of interval analysis even if it introduces further computational costs that are herein addressed developing tailored numerical strategies. In this paper an efficient method that allows to break down the computational costs of risk and uncertainty analyses considering intervals is presented. The method, implemented in an open source computational framework, is based on a very efficient Monte Carlo technique. Numerical results are delivered showing the applicability and efficiency of the proposed approach.
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures: Proceedings of the 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013. Boca Raton. ed. 2013. p. 2225-2232.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - An efficient strategy for computing interval expectations of risk
AU - De Angelis, M.
AU - Patelli, E.
AU - Beer, M.
PY - 2013
Y1 - 2013
N2 - In order to minimize risks it is of paramount importance to take into account the effects of uncertainties from the design stage. In fact, the knowledge about the behaviour of complex systems and future conditions is always incomplete. Risk-based optimization is a powerful and well-recognized tool for identification of the optimal (robust) design with a systematic consideration of uncertainty. More specifically, this approach looks for the best design solution, whilst minimizing the risk, thus considering the effects of uncertainties giving a measure of safety levels. However, traditional optimization procedures come out with a punctual (single) optimum that rarely can be translated in engineering solutions, leaving little or no room for manufacturing and operating tolerances. The optimization shall be given an even more rational connotation for treating the uncertainties that comprises set-wise quantities. Solution is found by means of interval analysis even if it introduces further computational costs that are herein addressed developing tailored numerical strategies. In this paper an efficient method that allows to break down the computational costs of risk and uncertainty analyses considering intervals is presented. The method, implemented in an open source computational framework, is based on a very efficient Monte Carlo technique. Numerical results are delivered showing the applicability and efficiency of the proposed approach.
AB - In order to minimize risks it is of paramount importance to take into account the effects of uncertainties from the design stage. In fact, the knowledge about the behaviour of complex systems and future conditions is always incomplete. Risk-based optimization is a powerful and well-recognized tool for identification of the optimal (robust) design with a systematic consideration of uncertainty. More specifically, this approach looks for the best design solution, whilst minimizing the risk, thus considering the effects of uncertainties giving a measure of safety levels. However, traditional optimization procedures come out with a punctual (single) optimum that rarely can be translated in engineering solutions, leaving little or no room for manufacturing and operating tolerances. The optimization shall be given an even more rational connotation for treating the uncertainties that comprises set-wise quantities. Solution is found by means of interval analysis even if it introduces further computational costs that are herein addressed developing tailored numerical strategies. In this paper an efficient method that allows to break down the computational costs of risk and uncertainty analyses considering intervals is presented. The method, implemented in an open source computational framework, is based on a very efficient Monte Carlo technique. Numerical results are delivered showing the applicability and efficiency of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=84892410847&partnerID=8YFLogxK
UR - https://doi.org/10.1201/b16387
M3 - Conference contribution
AN - SCOPUS:84892410847
SN - 9781138000865
SP - 2225
EP - 2232
BT - Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures
T2 - 11th International Conference on Structural Safety and Reliability, ICOSSAR 2013
Y2 - 16 June 2013 through 20 June 2013
ER -