Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes S Freitag, BT Cao, J Ninić, G Meschke Computers & Structures 207, 258-273, 2018 | 99 | 2018 |
Recurrent neural networks for uncertain time‐dependent structural behavior W Graf, S Freitag, M Kaliske, JU Sickert Computer‐Aided Civil and Infrastructure Engineering 25 (5), 322-323, 2010 | 79 | 2010 |
A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering J Ninić, S Freitag, G Meschke Tunnelling and Underground Space Technology 63, 12-28, 2017 | 74 | 2017 |
Recurrent neural networks for fuzzy data S Freitag, W Graf, M Kaliske Integrated Computer-Aided Engineering 18 (3), 265-280, 2011 | 66 | 2011 |
Structural analysis with fuzzy data and neural network based material description W Graf, S Freitag, JU Sickert, M Kaliske Computer‐Aided Civil and Infrastructure Engineering 27 (9), 640-654, 2012 | 61 | 2012 |
A hybrid RNN-GPOD surrogate model for real-time settlement predictions in mechanised tunnelling BT Cao, S Freitag, G Meschke Advanced Modeling and Simulation in Engineering Sciences 3 (1), 5, 2016 | 53 | 2016 |
Artificial neural network surrogate modelling for real-time predictions and control of building damage during mechanised tunnelling BT Cao, M Obel, S Freitag, P Mark, G Meschke Advances in Engineering Software 149, 102869, 2020 | 51 | 2020 |
Modeling of materials with fading memory using neural networks M Oeser, S Freitag International journal for numerical methods in engineering 78 (7), 843-862, 2009 | 51 | 2009 |
Lifetime prediction using accelerated test data and neural networks S Freitag, M Beer, W Graf, M Kaliske Computers & Structures 87 (19-20), 1187-1194, 2009 | 41 | 2009 |
Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data S Freitag, W Graf, M Kaliske, JU Sickert Computers & Structures 89 (21-22), 1971-1981, 2011 | 39 | 2011 |
A material description based on recurrent neural networks for fuzzy data and its application within the finite element method S Freitag, W Graf, M Kaliske Computers & Structures 124, 29-37, 2013 | 35 | 2013 |
Hybrid surrogate modelling for mechanised tunnelling simulations with uncertain data S Freitag, BT Cao, J Ninić, G Meschke International Journal of Reliability and Safety 9 (2/3), 154-173, 2015 | 30 | 2015 |
Multilevel surrogate modeling approach for optimization problems with polymorphic uncertain parameters S Freitag, P Edler, K Kremer, G Meschke International Journal of Approximate Reasoning 119, 81-91, 2020 | 28 | 2020 |
Reliability-based optimization of structural topologies using artificial neural networks S Freitag, S Peters, P Edler, G Meschke Probabilistic Engineering Mechanics 70, 103356, 2022 | 21 | 2022 |
Numerical simulation in mechanized tunneling in urban environments in the framework of a tunnel information model G Meschke, S Freitag, A Alsahly, J Ninic, S Schindler, C Koch Bauingenieur 89, 457-466, 2014 | 21* | 2014 |
Damage assessment concepts for urban structures during mechanized tunneling M Obel, A Marwan, A Alsahly, S Freitag, P Mark, G Meschke Bauingenieur 93, 482-491, 2018 | 19* | 2018 |
Fractional derivatives and recurrent neural networks in rheological modelling–part I: theory M Oeser, S Freitag International Journal of Pavement Engineering 17 (2), 87-102, 2016 | 18 | 2016 |
Real-time risk assessment of tunneling-induced building damage considering polymorphic uncertainty BT Cao, M Obel, S Freitag, L Heußner, G Meschke, P Mark ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A …, 2022 | 17 | 2022 |
Optimization approaches for the numerical design of structures under consideration of polymorphic uncertain data P Edler, S Freitag, K Kremer, G Meschke ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B …, 2019 | 17 | 2019 |
A particle swarm optimization approach for training artificial neural networks with uncertain data S Freitag, RL Muhanna, W Graf 5th International Conference on Reliable Engineering Computing (REC 2012 …, 2012 | 17 | 2012 |