期刊
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
卷 27, 期 9, 页码 640-654出版社
WILEY
DOI: 10.1111/j.1467-8667.2012.00779.x
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资金
- Deutsche Forschungsgemeinschaft (DFG - German Research Foundation) [FR 3044/1-1]
In the article, a new approach is presented utilizing artificial neural networks for uncertain time-dependent structural behavior. Recurrent neural networks (RNNs) for fuzzy data can be trained by uncertain experimental data to describe arbitrary stressstraintime dependencies. The benefit is a generalized formulation, which can be applied to describe the behavior of several materials without definition of a specific material model. Model-free material descriptions can be used as numerical efficient material formulations within the finite element method. To perform fuzzy or fuzzy stochastic finite element analyses, a new approach is introduced. An -level optimization is utilized for signal computation and training of RNNs for fuzzy data. The applicability is demonstrated by means of examples.
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