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

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• 作者：Jelena Ninić^a ; Steffen Freitag^b ; Gü ; nther Meschke^b ; ^{guenther.meschke@rub.de}
• 关键词：Mechanized tunnelling ; Finite element method ; Parameter identification ; Surrogate model ; Recurrent neural network ; Computational steering ; Tunnel boring machine ; Monitoring ; Settlements ; Real-time prediction
• 刊名：Tunnelling and Underground Space Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：12-28
• 全文大小：4255 K
• 卷排序：63

文摘

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.