Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes
A surrogate modelling strategy for predictions of interval settlement fields in real time during machine driven construction of tunnels, accounting for uncertain geotechnical parameters in terms of intervals, is presented in the paper. Artificial Neural Network and Proper Orthogonal Decomposition ap...
| Main Authors: | , , , |
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/42029/ |
| _version_ | 1848796402236260352 |
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| author | Freitag, S. Cao, B.T. Ninić, J. Meschke, G. |
| author_facet | Freitag, S. Cao, B.T. Ninić, J. Meschke, G. |
| author_sort | Freitag, S. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | A surrogate modelling strategy for predictions of interval settlement fields in real time during machine driven construction of tunnels, accounting for uncertain geotechnical parameters in terms of intervals, is presented in the paper. Artificial Neural Network and Proper Orthogonal Decomposition approaches are combined to approximate and predict tunnelling induced time variant surface settlement fields computed by a process-oriented finite element simulation model. The surrogate models are generated, trained and tested in the design (offline) stage of a tunnel project based on finite element analyses to compute the surface settlements for selected scenarios of the tunnelling process steering parameters taking uncertain geotechnical parameters by means of possible ranges (intervals) into account. The resulting mappings of time constant geotechnical interval parameters and time variant deterministic steering parameters onto the time variant interval settlement field are solved offline by optimisation and online by interval analyses approaches using the midpoint-radius representation of interval data. During the tunnel construction, 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. |
| first_indexed | 2025-11-14T19:47:25Z |
| format | Article |
| id | nottingham-42029 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:47:25Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-420292020-05-04T18:41:32Z https://eprints.nottingham.ac.uk/42029/ Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes Freitag, S. Cao, B.T. Ninić, J. Meschke, G. A surrogate modelling strategy for predictions of interval settlement fields in real time during machine driven construction of tunnels, accounting for uncertain geotechnical parameters in terms of intervals, is presented in the paper. Artificial Neural Network and Proper Orthogonal Decomposition approaches are combined to approximate and predict tunnelling induced time variant surface settlement fields computed by a process-oriented finite element simulation model. The surrogate models are generated, trained and tested in the design (offline) stage of a tunnel project based on finite element analyses to compute the surface settlements for selected scenarios of the tunnelling process steering parameters taking uncertain geotechnical parameters by means of possible ranges (intervals) into account. The resulting mappings of time constant geotechnical interval parameters and time variant deterministic steering parameters onto the time variant interval settlement field are solved offline by optimisation and online by interval analyses approaches using the midpoint-radius representation of interval data. During the tunnel construction, 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. Elsevier 2017-04-12 Article PeerReviewed Freitag, S., Cao, B.T., Ninić, J. and Meschke, G. (2017) Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes. Computers & Structures . ISSN 0045-7949 http://www.sciencedirect.com/science/article/pii/S0045794917302195 doi:10.1016/j.compstruc.2017.03.020 doi:10.1016/j.compstruc.2017.03.020 |
| spellingShingle | Freitag, S. Cao, B.T. Ninić, J. Meschke, G. Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title | Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title_full | Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title_fullStr | Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title_full_unstemmed | Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title_short | Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| title_sort | recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes |
| url | https://eprints.nottingham.ac.uk/42029/ https://eprints.nottingham.ac.uk/42029/ https://eprints.nottingham.ac.uk/42029/ |