This paper deals with an adaptation of artificial neural networks in the context of the reliability analysis of non-linear limit state functions. An extreme learning machine (ELM) that is categorized as a single-hidden-layer feed-forward neural network is considered in the present study. Using a trained ELM-based approximate meta-model, the reliability analysis is conducted in conjunction with Monte Carlo simulation. The ELM is compared with both single and multiple-hidden-layer back-propagation neural networks. A number of non-linear and large-dimensionality limit state functions are explored to support the proposed method in terms of approximation accuracy and reliability index.
|Number of pages||7|
|Journal||Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability|
|Publication status||Published - 2012 Oct 1|
All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality