Artificial intelligence - finite element method - hybrids for efficient nonlinear analysis of concrete structures

Authors

  • Michael A. Kraus ETH Zürich, Institute of Structural Engineering (IBK), Chair for Concrete Structures and Bridge Design, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland; ETH Zürich, Center for Augmented Computational Design in Architecture, Engineering and Construction, Design++ Initiative and Immersive Design Lab, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland
  • Rafael Bischof ETH Zürich, Institute of Structural Engineering (IBK), Chair for Concrete Structures and Bridge Design, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland
  • Walter Kaufmann ETH Zürich, Institute of Structural Engineering (IBK), Chair for Concrete Structures and Bridge Design, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland; ETH Zürich, Center for Augmented Computational Design in Architecture, Engineering and Construction, Design++ Initiative and Immersive Design Lab, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland
  • Karel Thoma ETH Zürich, Institute of Structural Engineering (IBK), Chair for Concrete Structures and Bridge Design, Stefano-Franscini-Platz 5, CH-8093 Zürich, Switzerland

DOI:

https://doi.org/10.14311/APP.2022.36.0099

Keywords:

concrete material model, machine and deep learning, nonlinear finite element method, surrogate modeling, uncertainty quantification

Abstract

Realistic structural analyses and optimisations using the non-linear finite element method are possible today yet suffer from being very time-consuming, particularly in case of reinforced concrete plates and shells. Hence such investigations are currently dismissed in the vast majority of cases in practice. The "Artificial Intelligence - Finite Element - Hybrids" project addresses the current unsatisfactory situation with an approach that combines non-linear finite element models for reinforced concrete shells with scientific machine learning algorithms to create hybrid AI-FEM models. The AI-based surrogate material model provides the material stiffness as well as the stress tensor for given concrete design parameters and the strain tensor. This paper reports on the current status of the project and findings of the calibration of the AI-based reinforced concrete material model. We successfully calibrated and evaluated k-nearest-neighbour, LGBM and ResNet algorithms and report their predictive capabilities. Finally, some light is shed on the future work of integrating the AI surrogate material models back into the finite element method in the course of the numerical analysis of reinforced concrete structures.

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Published

2022-08-18