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

verfasst von

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.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Hunan University
The University of Liverpool
Tongji University

Typ

Artikel

Journal

Computer Methods in Applied Mechanics and Engineering

Band

391

ISSN

0045-7825

Publikationsdatum

01.03.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Werkstoffmechanik, Maschinenbau, Allgemeine Physik und Astronomie, Angewandte Informatik, Numerische Mechanik

Elektronische Version(en)

https://doi.org/10.1016/j.cma.2021.114516 (Zugang: Geschlossen)