Review and application of Artificial Neural Networks models in reliability analysis of steel structures
By A. A. Chojaczyk, Â. Teixeira, Luis A. C. Neves, J. Cardoso, Carlos Soares. 2015
377 citations recorded by the source corpus.
This paper presents a survey on the development and use of Artificial Neural Network (ANN) models in structural reliability analysis. The survey identifies the different types of ANNs, the methods of structural reliability assessment that are typically used, the techniques proposed for ANN training set improvement and also some applications of ANN approximations to structural design and optimization problems. ANN models are then used in the reliability analysis of a ship stiffened panel subjected to uniaxial compression loads induced by hull girder vertical bending moment, for which the collapse strength is obtained by means of nonlinear finite element analysis (FEA). The approaches adopted combine the use of adaptive ANN models to approximate directly the limit state function with Monte Carlo simulation (MCS), first order reliability methods (FORM) and MCS with importance sampling (IS), for reliability assessment. A comprehensive comparison of the predictions of the different reliability methods with ANN based LSFs and classical LSF evaluation linked to the FEA is provided.
Subfields: Civil and Structural Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/d273e969bb1bb85c79cb2670fa99f20f90f49e04
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Stefano Marelli
- Dervis Karaboga
- Bruno Sudret
- Maliki Moustapha
- Anthony Nouy
- Xiaoping Du
- Zhen Hu
- Géraud Blatman
In Statistics and Probability
- Stefano Marelli
- Bruno Sudret
- Maliki Moustapha
- Anthony Nouy
- Xiaoping Du
- Zhen Hu
- Géraud Blatman
- Wolfgang Nowak
Citations in the corpus
- Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey (2018, methodology): “So, it has used widely in the literature (Almási et al. 2016; Chojaczyk et al. 2015; Kasar et al. 2016; Vasilakos et al. 2016; Vijayalakshmi and Girish 2015).”
- Active learning for structural reliability: Survey, general framework and benchmark (2021, background): “Even though most of the contributions are in a static scheme, the most recent ones consider adaptivity (Chojazyk et al., 2015; Kroetz et al., 2017).”