Multi-criteria optimization of life-cycle performance of structural systems under uncertainty

dc.contributor.authorM Frangopol, Dan
dc.contributor.authorM Okasha, Nader
dc.date.accessioned2022-03-07T11:16:00Z
dc.date.accessioned2023-08-19T08:11:23Z
dc.date.available2022-03-07T11:16:00Z
dc.date.available2023-08-19T08:11:23Z
dc.date.issued2009
dc.description.abstractPrediction of the life-cycle performance of structural systems must be accompanied with an efficient intervention planning procedure that assures the safe upkeep of structures. Multi-criteria optimization is an effective approach for conducting this procedure. Life-cycle performance of structural systems is typically quantified by means of performance indicators. The ability of the performance measures and their predictive models to accurately interpret and quantify the effects of applying maintenance interventions is necessary. The objective of this paper is to review recent advances in methods of multi-criteria optimization of life-cycle performance of structural systems under uncertainty. Two approaches for finding optimum maintenance strategies for deteriorating structural systems through multi-criteria optimization and using genetic algorithms are presented with applications. These approaches use different problem formulations and types of performance indicators.en_US
dc.identifier.citationFrangopol, D. M., & Okasha, N. M. (2009). Multi-criteria optimization of life-cycle performance of structural systems under uncertainty. Risk and Decision Analysis in Maintenance Optimization and Flood Management, 99.en_US
dc.identifier.doihttps://doi.org/10.3233/978-1-60750-522-8-99
dc.identifier.urihttps://edms.wexl.in/handle/1/2849
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.subjectStructural systemsen_US
dc.subjectPerformance indicators.en_US
dc.subjectMulti-criteria optimizationen_US
dc.subjectAccompanieden_US
dc.titleMulti-criteria optimization of life-cycle performance of structural systems under uncertaintyen_US
dc.title.alternativejournal Articalen_US
dc.typeBooken_US

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