@inproceedings{7ac830da44694bf79415c18aa05ccb5c,
title = "Efficient Monte Carlo algorithm for rare failure event simulation",
abstract = "Studying failure scenarios allows one to gain insights into their cause and consequence, providing information for effective mitigation, contingency planning and improving system resilience. A new efficient algorithm is here proposed to solve applications where an expensive-to-evaluate computer model is involved. The algorithms allows to generate the conditional samples for the Subset simulation by representing each random variable by an arbitrary number of hidden variables. The resulting algorithm is very simple yet powerful and it does not required the use of the Markov Chain Monte Carlo method. The proposed algorithm has been implemented in a open source general purpose software, OpenCossan allowing the solution of large scale problems of industrial interest by taking advantages of High Performance Computing facilities. The applicability and flexibility of the proposed approach is shown by solving a number of different problems.",
keywords = "Monte Carlo methods, failure scenarios, Markov Chain Monte Carlo method, computer model, algorithms, reliability analysis, contingency planning",
author = "Edoardo Patelli and Au, {Siu Kui}",
year = "2015",
month = jul,
day = "15",
language = "English",
series = "12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015",
publisher = "University of British Columbia",
booktitle = "12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2015",
address = "Canada",
note = "12th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2012 ; Conference date: 12-07-2015 Through 15-07-2015",
}