Reference : Poroelastic material characterisation by means of Artificial Neural Network
Scientific Presentations in Universities or Research Centers : Scientific presentation in universities or research centers
Engineering, computing & technology : Civil engineering
Engineering, computing & technology : Materials science & engineering
Engineering, computing & technology : Mechanical engineering
Computational Sciences
http://hdl.handle.net/10993/41912
Poroelastic material characterisation by means of Artificial Neural Network
English
Dehghani, Hamidreza mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Zilian, Andreas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
13-Nov-2019
39
Team meeting of Andreas Zilian
13-11-2019
Belval
Luxembourg
[en] Artificial intelligence ; Artificial Neural Network ; Poroelasticity ; multiscale analysis ; machine learning ; AI in mechanics ; model parameter characterisation ; material characterisation
[en] Poroelastic problems require multiscale and multiphysics techniques that are expensive and time-consuming, which result in either several simplifications or costly experimental tests. The latter motivates us to develop a more efficient approach to address more complex problems with an acceptable computational cost.
In this manuscript, first, the necessary equations derived from Asymptotic homogenisation for poroelastic media are mentioned. Then, the variational formulation of the cell problems is carried out and solved by the open-source FE package FEniCS. This is followed by presenting the advantages and downsides of macroscale properties identification via asymptotic homogenisation and the application of Artificial Neural Network (ANN) to solve the issues stated as its downsides by means of bypassing the process of solving the cell problems. Finally, we study a practical example, namely, spatial dependent porosity (in macroscale) to demonstrate the feasibility of using the provided framework to include more details. Further applications, including growth and remodelling, are subjects of future articles.
Researchers ; Professionals
http://hdl.handle.net/10993/41912

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