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Steffen Freitag
Researcher at Ruhr University Bochum
Publications - 56
Citations - 604
Steffen Freitag is an academic researcher from Ruhr University Bochum. The author has contributed to research in topics: Artificial neural network & Recurrent neural network. The author has an hindex of 11, co-authored 47 publications receiving 432 citations. Previous affiliations of Steffen Freitag include Dresden University of Technology.
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Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior
TL;DR: An approach is introduced which permits the numerical prediction of future structural responses in dependency of uncertain load processes and environmental influences and an efficient solution for network training and prediction is developed utilizing α‐cuts and interval arithmetic.
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Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes
TL;DR: The surrogate model is designed to be used in real-time to predict interval fields of the surface settlements in each stage of the advancement of the tunnel boring machine for selected realisations of the steering parameters to support the steering decisions of the machine driver.
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Recurrent neural networks for fuzzy data
TL;DR: A model-free approach for data mining in engineering based on artificial neural networks based on fuzzy fractional rheological material model for the identification and prediction of time-dependent structural behavior under dynamic loading is presented.
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Structural Analysis with Fuzzy Data and Neural Network Based Material Description
TL;DR: A new approach is presented utilizing artificial neural networks for uncertain time‐dependent structural behavior and an ‐level optimization is utilized for signal computation and training of RNNs for fuzzy data.
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A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering
TL;DR: The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling with the computational efficiency of surrogate (or meta) models based on artificial neural networks to accurately describe the complex geological and mechanical interactions of the Tunnelling process.