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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2005.4
- 수록면
- 251 - 256 (6page)
이용수
초록· 키워드
오류제보하기
Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fatal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input-output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.
목차
Abstract
1. Introduction
2. Multilayer Feedforward Neural Networks
3. Material Models
4. Neural Constitutive Modeling
5. Numerical Examples
6. Conclusions
References
저자소개
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UCI(KEPA) : I410-ECN-0101-2009-028-014779985