{"title":"Application of QMU to structural analysis considering uncertainty","authors":"Chao Xie, Hongzhong Huang, F. Wei","doi":"10.1109/ICQR2MSE.2012.6246425","DOIUrl":null,"url":null,"abstract":"Quantification of Margins and Uncertainties (QMU) is a methodology for assessing the confidence in the performance of complex systems, and has been applied to general engineering areas. In this paper, the concept and process of QMU is introduced at first, and then an implementation framework of QMU is proposed for structural analysis considering uncertainty. The framework is a synthesis of several algorithms, including a sampling method, surrogate model with uncertainty, and finite element analysis. An example is presented to illustrate the capability of the developed framework. The results show that the framework is feasible and suitable for the quantification of uncertainty in structural analysis.","PeriodicalId":401503,"journal":{"name":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICQR2MSE.2012.6246425","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Quantification of Margins and Uncertainties (QMU) is a methodology for assessing the confidence in the performance of complex systems, and has been applied to general engineering areas. In this paper, the concept and process of QMU is introduced at first, and then an implementation framework of QMU is proposed for structural analysis considering uncertainty. The framework is a synthesis of several algorithms, including a sampling method, surrogate model with uncertainty, and finite element analysis. An example is presented to illustrate the capability of the developed framework. The results show that the framework is feasible and suitable for the quantification of uncertainty in structural analysis.