For structures that only the predicted bounds of uncertainties are available,this study proposes a Bayesianmethod to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex mo...For structures that only the predicted bounds of uncertainties are available,this study proposes a Bayesianmethod to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data.According to the given interval ranges of uncertainties,we determine the initial characteristic parameters of a multi-ellipsoid convex set.Moreover,to update the plausibility of characteristic parameters,a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed.Then,an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be achieved.The credible non-probabilistic reliability index is calculated based on the Kriging-based surrogate model of the performance function.Several numerical examples are presented to validate the proposed Bayesian updating method.展开更多
Reliability of mechanical structures is an important issue in the engineering field,especially in the field of aerospace and other high-tech sophisticated fields.Up to now,reliability-based evaluation and optimization...Reliability of mechanical structures is an important issue in the engineering field,especially in the field of aerospace and other high-tech sophisticated fields.Up to now,reliability-based evaluation and optimization methods have been widely applied in the safety analysis and design of mechanical structures.Many advanced algorithms for significantly improving the calculation efficiency and accuracy of solutions have been developed and gained successful applications in real engineering problems.Recently,with the advantages of strong versatility and well global-searching ability,more and more attention has been paid to the intelligent algorithm of mechanical structure reliability optimization design.展开更多
基金This work was supported financially by the National Key R&D Program of China(2017YFB0203604)the National Natural Science Foundation of China(11972104,11772077)the Liaoning Revitalization Talents Program(XLYC1807187).
文摘For structures that only the predicted bounds of uncertainties are available,this study proposes a Bayesianmethod to logically evaluate the nonprobabilistic reliability of structures based on multi-ellipsoid convex model and performance test data.According to the given interval ranges of uncertainties,we determine the initial characteristic parameters of a multi-ellipsoid convex set.Moreover,to update the plausibility of characteristic parameters,a Bayesian network for the information fusion of prior uncertainty knowledge and subsequent performance test data is constructed.Then,an updated multi-ellipsoid set with the maximum likelihood of the performance test data can be achieved.The credible non-probabilistic reliability index is calculated based on the Kriging-based surrogate model of the performance function.Several numerical examples are presented to validate the proposed Bayesian updating method.
基金This work has been supported by the Natural Science Foundation of Shaanxi Province(2019JM-377)the Fundamental Research Funds for the Central Universities(NWPU-310202006zy007).
文摘Reliability of mechanical structures is an important issue in the engineering field,especially in the field of aerospace and other high-tech sophisticated fields.Up to now,reliability-based evaluation and optimization methods have been widely applied in the safety analysis and design of mechanical structures.Many advanced algorithms for significantly improving the calculation efficiency and accuracy of solutions have been developed and gained successful applications in real engineering problems.Recently,with the advantages of strong versatility and well global-searching ability,more and more attention has been paid to the intelligent algorithm of mechanical structure reliability optimization design.
