Influence of the Cutterhead Configuration and Operation Parameters on the Face Stability of EPB Shield Tunnels in Dry Granular Soils

Hu, Xiongyu; Cheng, Jinguo; Ju, J. W.

doi:10.1061/(asce)gm.1943-5622.0002008

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…In selecting the indexes for evaluating the excavation face stability, the principles should be easy accessibility and representativeness. After analysing a series of related research results, the buried depth h, internal friction angle φ, water content w and cohesion c were selected [25,[39][40][41][42][43][44][45][46]. Besides, the thickness of overburdened water will also affect the excavation face to a certain extent.…”

Section: (D) Importance Analysis Of the Evaluation Indicatorsmentioning

confidence: 99%

“…Some commonly used numerical simulation methods include discrete, finite, and finite difference methods. In a numerical simulation, the accuracy of the results can also be controlled by setting calculation standards and cyclic conditions and comparing them with actual parameters [10,[25][26][27]. Nevertheless, due to the assumptions of numerical simulations, the degree of reduction of actual working conditions is low.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining

Xue

et al. 2023

Phil. Trans. R. Soc. A.

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Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10 −4 is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability. This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.

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