A GRU-based ensemble learning method for time-variant uncertain structural response analysis

authored by: Kun Zhang, Ning Chen, Jian Liu, Michael Beer
Abstract: 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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): Hunan University
University of Liverpool
Tongji University
Type: Article
Journal: Computer Methods in Applied Mechanics and Engineering
Volume: 391
ISSN: 0045-7825
Publication date: 01.03.2022
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Mechanics of Materials, Mechanical Engineering, Physics and Astronomy(all), Computer Science Applications, Computational Mechanics
Electronic version(s): https://doi.org/10.1016/j.cma.2021.114516 (Access: Closed)