Artificial neural networks in structural dynamics: A new modular radial basis function approach vs. convolutional and feedforward topologies

The aim of the present study is to develop a series of artificial neural networks (ANN) and to determine, by comparison to experiments, which type of neural network is able to predict the measured structural deformations most accurately. For this approach, three different ANNs are proposed. Firstly,...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 364; s. 112989
Hlavní autoři: Stoffel, Marcus, Gulakala, Rutwik, Bamer, Franz, Markert, Bernd
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.06.2020
Elsevier BV
Témata:
Artificial neural network
Artificial neural networks
Convolutional networks
Modular structures
Neural networks
Numerical prediction
Radial basis function
Radial basis functions
Shock tube experiments
Structural mechanics
Topology
Radial basis functions
74K20
Structural mechanics
74-05
Artificial neural network
Shock tube experiments
Convolutional networks
ISSN:0045-7825, 1879-2138
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Shrnutí:The aim of the present study is to develop a series of artificial neural networks (ANN) and to determine, by comparison to experiments, which type of neural network is able to predict the measured structural deformations most accurately. For this approach, three different ANNs are proposed. Firstly, the classical form of an ANN in the form of a feedforward neural network (FFNN). In the second approach a new modular radial basis function neural network (RBFNN) is proposed and the third network consists of a deep convolutional neural network (DCNN). By means of comparative calculations between neural network enhanced numerical predictions and measurements, the applicability of each type of network is studied. •Three types of artificial neural networks are proposed for structural dynamics.•All calculated results are verified by means of shock tube experiments.•A new modular radial basis function network is developed.•The convolutional neural networks capture the loading and deformation history.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2020.112989