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dc.contributor.authorRoyset, J.O.
dc.contributor.authorPolak, E.
dc.date2004
dc.date.accessioned2016-05-18T20:41:06Z
dc.date.available2016-05-18T20:41:06Z
dc.date.issued2004
dc.identifier.citationProbabilistic Engineering Mechanics 19 (2004) 331–343en_US
dc.identifier.urihttp://hdl.handle.net/10945/48694
dc.descriptionThe article of record as published may be found at http://dx.doi.org/10.1016/j.probengmech.2004.03.001en_US
dc.description.abstractAn algorithm for reliability-based optimal design is developed using sampling techniques for estimating the failure probability. The algorithm applies a new method for sensitivity calculations of the failure probability. Initially, the estimates of the failure probability are coarse. As the algorithm progresses towards an optimal design, the number of sample points is increased in an adaptive way leading to better estimates of the failure probability. The algorithm is proven to converge to an optimal design. The applicability of the algorithm is shown in an example from the area of highway bridge design.en_US
dc.description.sponsorshipResearch Associateship Program at the National Research Councilen_US
dc.description.sponsorshipTaisei Chair in Civil Engineering at UC Berkeleyen_US
dc.description.sponsorshipNational Science Foundation grant ECS-9900985en_US
dc.format.extent13 p.en_US
dc.publisherElsevier Ltd.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleReliability-based optimal design using sample average approximationsen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentOperations Research (OR)en_US
dc.subject.authorReliability-based optimal designen_US
dc.subject.authorSample average approximationsen_US
dc.subject.authorMonte-Carlo simulationen_US
dc.subject.authorBridge designen_US
dc.description.funderResearch Associateship Program at the National Research Councilen_US
dc.description.funderTaisei Chair in Civil Engineering at UC Berkeleyen_US
dc.description.funderNational Science Foundation grant ECS-9900985en_US


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