Reliability-based design: Artificial neural networks and double-loop reliability-based optimization approaches. (March 2018)

• Record Type: Journal Article
• Authors: Lehký, D.; Slowik, O.; Novák, D.
• Is Part Of: Advances in engineering software. Volume 117(2018)
• Page Start: 123
• Page End: 135
• Publication Date: 2018-03
• Subjects: Inverse reliability problem -- Artificial neural network -- Double-loop reliability-based optimization -- Latin hypercube sampling -- First order reliability method; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2017.06.013
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Two approaches for solving a reliability-based design problem are proposed. Artificial neural network based inverse reliability method has been implemented. Double-loop reliability-based optimization method has been implemented. Verification using examples of structural design was carried out. Abstract: 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 post-tensioned composite bridge. The advantages and disadvantages of the approaches are discussed.