Simulation-Based Analysis of Tunnel Boring Machine Performance in Tunneling Excavation

This paper develops a hybrid simulation approach that integrates a dynamic default tree (DFT) and discrete-time Bayesian network (DTBN) to support tunnel boring machine (TBM) performance prediction and diagnosis. A causal network model consisting of 20 nodes is built to simulate shield cutter head failure over time during the TBM operation. A total of three indicators, namely, T(10), T(20), and mean time to failure (MTTF), are proposed to transfer the complicated probability distribution into a specific and useful number to explicitly measure the performance level of system unreliability. One of the tunneling projects recently completed in the Wuhan metro system in China has been selected as a case study to verify the applicability of the developed approach. The results indicate that the developed approach is capable of performing not only a feedforward analysis for the estimation of the TBM performance, but also feedback analysis, given that a low performance or failure is observed. This approach provides a powerful potential solution to modeling and analyzing various kinds of system component behaviors and interactions in a complex project environment.

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  • English

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  • Accession Number: 01606552
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Jun 17 2016 3:26PM