@inproceedings{9a7db554383945cab2af5bd4b9bb16bf,
title = "Time-dependent probability of exceeding restoration levels in resilience analysis",
abstract = "Resilience typically refers to the ability of a system to adapt to changing conditions, withstand and rapidly recover from disruptions due to extreme/rare events. This paper proposes a methodology to predict the recovery process of a selected system given past recovery data and estimate the probability of exceeding a target value of functionality at any time. A Bayesian inference is used to update the model parameters as new data become available while the recovery process is in progress. The methodology is general and can be applied to continuous recovery processes such as those of economic or natural systems, as well as to discrete recovery process such as those of engineering systems. As an illustration, the proposed methodology is implemented considering a bridge restoration following seismic damage.",
keywords = "Decision Support, Metrics, Recovery, Reliability analysis, Resilience",
author = "F. Nocera and P. Gardoni and Cimellaro, {G. P.}",
note = "Publisher Copyright: {\textcopyright} 2018 Taylor & Francis Group, London.; 9th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2018 ; Conference date: 09-07-2018 Through 13-07-2018",
year = "2018",
language = "English (US)",
isbn = "9781138730458",
series = "Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges - Proceedings of the 9th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2018",
publisher = "CRC Press/Balkema",
pages = "2012--2019",
editor = "Nigel Powers and Frangopol, {Dan M.} and Riadh Al-Mahaidi and Colin Caprani",
booktitle = "Maintenance, Safety, Risk, Management and Life-Cycle Performance of Bridges - Proceedings of the 9th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2018",
}