Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches

Abstract : The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation. Efficiency of both approaches is presented in numerical example, advantages and disadvantages are discussed.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-01391333
Contributor : Hal Ifip <>
Submitted on : Thursday, November 3, 2016 - 11:00:54 AM
Last modification on : Friday, December 1, 2017 - 1:16:36 AM
Long-term archiving on : Saturday, February 4, 2017 - 1:07:41 PM

File

978-3-662-44654-6_34_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

David Lehký, Ondřej Slowik, Drahomír Novák. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.344-353, ⟨10.1007/978-3-662-44654-6_34⟩. ⟨hal-01391333⟩

Share

Metrics

Record views

218

Files downloads

264