A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering
The paper proposes a novel computational method for real-time simulation and monitoring-based predictions during the construction of machine-driven tunnels to support decisions concerning the steering of tunnel boring machines (TBMs). The proposed technique combines the capacity of a process-oriente...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2017
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/39633/ |
| _version_ | 1848795880436531200 |
|---|---|
| author | Ninic, Jelena Freitag, Steffen Meschke, Günther |
| author_facet | Ninic, Jelena Freitag, Steffen Meschke, Günther |
| author_sort | Ninic, Jelena |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The paper proposes a novel computational method for real-time simulation and monitoring-based predictions during the construction of machine-driven tunnels to support decisions concerning the steering of tunnel boring machines (TBMs). The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling to accurately describe the complex geological and mechanical interactions of the tunnelling process with the computational efficiency of surrogate (or meta) models based on artificial neural networks. The process-oriented 3D simulation model with updated model parameters based on acquired monitoring data during the advancement process is used in combination with surrogate models to determine optimal tunnel machine-related parameters such that tunnelling-induced settlements are kept below a tolerated level within the forthcoming process steps. The performance of the proposed strategy is applied to the Wehrhahn-line metro project in Düsseldorf, Germany and compared with a recently developed approach for real-time steering of TBMs, in which only surrogate models are used. |
| first_indexed | 2025-11-14T19:39:07Z |
| format | Article |
| id | nottingham-39633 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:39:07Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-396332020-05-04T19:58:27Z https://eprints.nottingham.ac.uk/39633/ A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering Ninic, Jelena Freitag, Steffen Meschke, Günther The paper proposes a novel computational method for real-time simulation and monitoring-based predictions during the construction of machine-driven tunnels to support decisions concerning the steering of tunnel boring machines (TBMs). The proposed technique combines the capacity of a process-oriented 3D simulation model for mechanized tunnelling to accurately describe the complex geological and mechanical interactions of the tunnelling process with the computational efficiency of surrogate (or meta) models based on artificial neural networks. The process-oriented 3D simulation model with updated model parameters based on acquired monitoring data during the advancement process is used in combination with surrogate models to determine optimal tunnel machine-related parameters such that tunnelling-induced settlements are kept below a tolerated level within the forthcoming process steps. The performance of the proposed strategy is applied to the Wehrhahn-line metro project in Düsseldorf, Germany and compared with a recently developed approach for real-time steering of TBMs, in which only surrogate models are used. Elsevier 2017-03 Article PeerReviewed Ninic, Jelena, Freitag, Steffen and Meschke, Günther (2017) A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering. Tunnelling and Underground Space Technology, 63 . pp. 12-28. ISSN 0886-7798 Mechanized tunnelling; Finite element method; Parameter identification; Surrogate model; Recurrent neural network; Computational steering; Tunnel boring machine; Monitoring; Settlements; Real-time prediction http://www.sciencedirect.com/science/article/pii/S0886779815302972 doi:10.1016/j.tust.2016.12.004 doi:10.1016/j.tust.2016.12.004 |
| spellingShingle | Mechanized tunnelling; Finite element method; Parameter identification; Surrogate model; Recurrent neural network; Computational steering; Tunnel boring machine; Monitoring; Settlements; Real-time prediction Ninic, Jelena Freitag, Steffen Meschke, Günther A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title | A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title_full | A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title_fullStr | A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title_full_unstemmed | A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title_short | A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering |
| title_sort | hybrid finite element and surrogate modelling approach for simulation and monitoring supported tbm steering |
| topic | Mechanized tunnelling; Finite element method; Parameter identification; Surrogate model; Recurrent neural network; Computational steering; Tunnel boring machine; Monitoring; Settlements; Real-time prediction |
| url | https://eprints.nottingham.ac.uk/39633/ https://eprints.nottingham.ac.uk/39633/ https://eprints.nottingham.ac.uk/39633/ |