Details
| Original language | English |
|---|---|
| Article number | 114516 |
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 391 |
| Issue number | 391 |
| Early online date | 25 Jan 2022 |
| Publication status | Published - 1 Mar 2022 |
Abstract
Keywords
- Active learning, Ensemble learning, Gaussian process, GRU, Time-variant response
ASJC Scopus subject areas
- Engineering(all)
- Mechanics of Materials
- Engineering(all)
- Mechanical Engineering
- Physics and Astronomy(all)
- General Physics and Astronomy
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Computational Mechanics
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 391, No. 391, 114516, 01.03.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A GRU-based ensemble learning method for time-variant uncertain structural response analysis
AU - Zhang, Kun
AU - Chen, Ning
AU - Liu, Jian
AU - Beer, Michael
N1 - Funding Information: The paper is supported by the National Natural Science Foundation of China (Grant No. 51905162 ), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51621004 ) and the Fundamental Research Funds for the Central Universities ( 531107051148 ). The author would also like to thank reviewers for their valuable suggestions.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Owing to the influence of manufacturing and assembly errors, material performance degradation, external loads and unpredictability of the environment during service, structural response analysis should consider the time-invariant uncertainties and time-variant uncertainties simultaneously. In this paper, a mixed uncertainty model with random variable and stochastic process is adopted to handle this issue. A time-variant uncertain structural response analysis method is proposed based on recurrent neural network using gated recurrent units (GRU) combined with ensemble learning. In the proposed method, by performing Latin hypercube sampling (LHS) of random variables, multiple GRU networks can be trained to estimate the time-variant system response under fixed random variables. During the process of training GRU models, an active learning strategy is developed and applied to improve model accuracy and reduce training samples. On this basis, a set of augmented data is generated using the trained GRU models. Then the mapping relationship between random variables and structural responses through the Gaussian process (GP) regression is built accordingly. Eventually, the global surrogate model of time-variant uncertain structural response can be obtained by integrating the GRU networks and the GP models. Two numerical examples are used to demonstrate the effectiveness and accuracy of the proposed method. The results indicate that the proposed method can effectively calculate the expectation and standard deviation of the system response under the mixed uncertainty model with random variables and stochastic processes. In addition, it has higher computational efficiency under the premise of ensuring the calculation accuracy.
AB - Owing to the influence of manufacturing and assembly errors, material performance degradation, external loads and unpredictability of the environment during service, structural response analysis should consider the time-invariant uncertainties and time-variant uncertainties simultaneously. In this paper, a mixed uncertainty model with random variable and stochastic process is adopted to handle this issue. A time-variant uncertain structural response analysis method is proposed based on recurrent neural network using gated recurrent units (GRU) combined with ensemble learning. In the proposed method, by performing Latin hypercube sampling (LHS) of random variables, multiple GRU networks can be trained to estimate the time-variant system response under fixed random variables. During the process of training GRU models, an active learning strategy is developed and applied to improve model accuracy and reduce training samples. On this basis, a set of augmented data is generated using the trained GRU models. Then the mapping relationship between random variables and structural responses through the Gaussian process (GP) regression is built accordingly. Eventually, the global surrogate model of time-variant uncertain structural response can be obtained by integrating the GRU networks and the GP models. Two numerical examples are used to demonstrate the effectiveness and accuracy of the proposed method. The results indicate that the proposed method can effectively calculate the expectation and standard deviation of the system response under the mixed uncertainty model with random variables and stochastic processes. In addition, it has higher computational efficiency under the premise of ensuring the calculation accuracy.
KW - Active learning
KW - Ensemble learning
KW - Gaussian process
KW - GRU
KW - Time-variant response
UR - http://www.scopus.com/inward/record.url?scp=85123680384&partnerID=8YFLogxK
U2 - 10.1016/j.cma.2021.114516
DO - 10.1016/j.cma.2021.114516
M3 - Article
VL - 391
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
SN - 0045-7825
IS - 391
M1 - 114516
ER -