A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering

Ninic, Jelena; Freitag, Steffen; Meschke, G�nther

Abstract

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.

Citation

Ninic, J., Freitag, S., & Meschke, G. (2017). A hybrid finite element and surrogate modelling approach for simulation and monitoring supported TBM steering. Tunnelling and Underground Space Technology, 63, 12-28. https://doi.org/10.1016/j.tust.2016.12.004

• Journal Article Type: Article
• Acceptance Date: Dec 8, 2016
• Online Publication Date: Dec 23, 2016
• Publication Date: 2017-03
• Deposit Date: Jan 5, 2017
• Publicly Available Date: Jan 5, 2017
• Journal: Tunnelling and Underground Space Technology
• Print ISSN: 0886-7798
• Electronic ISSN: 1878-4364
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 63
• Pages: 12-28
• DOI: https://doi.org/10.1016/j.tust.2016.12.004
• Keywords: Mechanized tunnelling; Finite element method; Parameter identification; Surrogate model; Recurrent neural network; Computational steering; Tunnel boring machine; Monitoring; Settlements; Real-time prediction
• Public URL: https://nottingham-repository.worktribe.com/output/970391
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S0886779815302972
• Contract Date: Jan 5, 2017