Reliability-based design optimization using kriging surrogates and subset simulation
Structural and Multidisciplinary Optimization
Structural and Multidisciplinary Optimization
Optimal performance-based design of wind sensitive tall buildings considering uncertainties
Computers and Structures
An adaptive decoupling approach for reliability-based design optimization
Computers and Structures
Adaptive virtual support vector machine for reliability analysis of high-dimensional problems
Structural and Multidisciplinary Optimization
An optimal shifting vector approach for efficient probabilistic design
Structural and Multidisciplinary Optimization
A local adaptive sampling method for reliability-based design optimization using Kriging model
Structural and Multidisciplinary Optimization
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Reliability-based design optimization (RBDO) dealing with variation of output induced by uncertainty of design variables needs computationally expensive reliability analysis to calculate failure probability. Metamodel-based RBDO is one of emerging techniques used to overcome computational drawback. In this research, constraint boundary sampling is proposed to build metamodel that can predict optimum point accurately while satisfying constraints. Constraint boundary sampling is sequentially to locate sample points along constraint boundary by using kriging model and its mean squared error. Metamodel-based RBDOs with constraint boundary sampling are compared with that with conventional space-filling sampling.