Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1285-1298 |
| Seitenumfang | 14 |
| Fachzeitschrift | Frontiers of Structural and Civil Engineering |
| Jahrgang | 14 |
| Ausgabenummer | 6 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Extern publiziert | Ja |
Abstract
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Tief- und Ingenieurbau
- Ingenieurwesen (insg.)
- Architektur
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in: Frontiers of Structural and Civil Engineering, Jahrgang 14, Nr. 6, 2020, S. 1285-1298.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Multiscale computation on feedforward neural network and recurrent neural network
AU - Li, B
AU - Zhuang, XY
N1 - Funding information: The authors acknowledge the support from the National Natural Science Foundation of China (Grant No. 11772234).
PY - 2020
Y1 - 2020
N2 - Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.
AB - Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.
KW - multiscale method
KW - constitutive model
KW - feedforward neural network
KW - recurrent neural network
KW - CONSISTENT CLUSTERING ANALYSIS
KW - CONSTITUTIVE PROPERTIES
KW - MODEL
KW - BEHAVIOR
KW - HOMOGENIZATION
KW - PLASTICITY
UR - http://www.scopus.com/inward/record.url?scp=85098720724&partnerID=8YFLogxK
U2 - 10.1007/s11709-020-0691-7
DO - 10.1007/s11709-020-0691-7
M3 - Article
VL - 14
SP - 1285
EP - 1298
JO - Frontiers of Structural and Civil Engineering
JF - Frontiers of Structural and Civil Engineering
SN - 2095-2430
IS - 6
ER -