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 -