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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| Vydané v: | Computer methods in applied mechanics and engineering Ročník 364; s. 112989 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
01.06.2020
Elsevier BV |
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| ISSN: | 0045-7825, 1879-2138 |
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| Abstract | 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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| AbstractList | 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. 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. |
| ArticleNumber | 112989 |
| Author | Markert, Bernd Stoffel, Marcus Gulakala, Rutwik Bamer, Franz |
| Author_xml | – sequence: 1 givenname: Marcus orcidid: 0000-0001-8756-2310 surname: Stoffel fullname: Stoffel, Marcus email: stoffel@iam.rwth-aachen.de – sequence: 2 givenname: Rutwik surname: Gulakala fullname: Gulakala, Rutwik – sequence: 3 givenname: Franz orcidid: 0000-0002-8587-6591 surname: Bamer fullname: Bamer, Franz – sequence: 4 givenname: Bernd surname: Markert fullname: Markert, Bernd |
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| Keywords | Radial basis functions 74K20 Structural mechanics 74-05 Artificial neural network Shock tube experiments Convolutional networks |
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| SubjectTerms | 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 |
| Title | Artificial neural networks in structural dynamics: A new modular radial basis function approach vs. convolutional and feedforward topologies |
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