Abstract
The aim of the paper is to present a newly developed approach for reliability-based design optimization. It is based on double loop framework where the outer loop of algorithm covers the optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop. Innovation of suggested approach is in application of newly developed optimization strategy based on multilevel simulation using an advanced Latin Hypercube Sampling technique. This method is called Aimed multilevel sampling and it is designated for optimization of problems where only limited number of simulations is possible to perform due to enormous com- putational demands.
Highlights
Reliability-based optimization is a demanding discipline in which it is necessary to combine the optimization approaches and reliability assessment of structures [1]
Some particular parts of the reliability calculations can be even formulated as an optimization problem
This paper presents a newly developed approach to reliability-based design optimization
Summary
Reliability-based optimization is a demanding discipline in which it is necessary to combine the optimization approaches and reliability assessment of structures [1]. A connection of model optimization with its reliability assessment in the form of optimization constraint is a challenging issue. Thanks to the development of computer technology and stochastic, simulation and approximation methods themselves is such connection of optimization process with reliability assessment possible nowadays [3]. This paper presents a newly developed approach to reliability-based design optimization. It is based on double loop framework where outer loop of algorithm covers optimization part of process of reliability-based optimization and reliability constrains are calculated in inner loop [4].
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