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Reliability-based design: Artificial neural networks and double-loop reliability-based optimization approaches
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DOI：10.1016/j.advengsoft.2017.06.013.png)
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Two advanced optimization approaches to solving a reliability-based design problem are presented. The first approach is based on the utilization of an artificial neural network and a small-sample simulation technique. The second approach considers an inverse reliability task as a reliability-based optimization task using a double-loop optimization method based on small-sample simulation. Both techniques utilize Latin hypercube sampling with correlation control. The efficiency of both approaches is tested using three numerical examples of structural design - a cantilever beam, a reinforced concrete slab and a posttensioned composite bridge. The advantages and disadvantages of the approaches are discussed. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Inverse reliability problem
Artificial neural network
Double-loop reliability-based optimization
Latin hypercube sampling
First order reliability method
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