Investigation of ultimate bearing capacity of shield tunnel based on concrete damage model

Liu, Jianwen; Shi, Chenghua; Gong, Chenjie; Lei, Mingfeng; Wang, Zuxian; Peng, Zhu; Cao, Chengyong

doi:10.1016/j.tust.2022.104510

Cited by 20 publications

(1 citation statement)

References 29 publications

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“…There are currently two basic approaches for building a prediction model for the Earth pressure within a chamber using tunneling parameters. According to Hooke's rule of conservation of excavation and dumping volume of soil, one is to develop a mathematical model of the Earth pressure in chamber prediction (Liu et al, 2022). The alternative way is to construct a prediction model of the Earth pressure in a chamber based on tunneling characteristics identified during the tunneling process using neural networks and other more intelligent methods (Shi et al, 2008;Liu et al, 2011).…”

Section: Open Accessmentioning

confidence: 99%

Prediction method of shield tunneling parameters in pebble stratum formed by weathered granite and quartzite

Wang

Zhao

et al. 2023

Front. Earth Sci.

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The pebble formation formed by weathered quartzite and granite is usually of high strength, strong permeability and poor self stability, which brings great difficulties to shield tunneling. It is necessary to adjust the tunneling parameters at any time to prevent the consequences of instability of the tunnel face, loss of stratum and surface settlement caused by the imbalance of the soil bin pressure. GA algorithm is embedded into PSO algorithm for parameter optimization, and grey theory is combined to establish the prediction model of soil chamber pressure based on grey least square support vector machine, which can solve the problem of difficult control of tunneling parameters in pebble stratum formed by weathered quartzite and granite. Research indicates: GA-PSO-GLSSVM chamber Earth pressure prediction model enhances the EPB chamber Earth pressure forecast accuracy in complicated strata by integrating the global search capability of the GA algorithm, the quick convergence of the PSO algorithm, and the anti-disturbance capability of the GM model. The GA-PSO-GLSSVM model has high goodness-of-fit and accuracy compared with other prediction models. This model can successfully prevent the a series of undesirable consequences such as destabilization of the palm surface, missing strata and settlement due to ground surface due to pressure imbalance in the soil chamber pressure. The research results can provide reference for EPB shield tunneling parameter control in Grade V surrounding rock.

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Section: Open Accessmentioning

confidence: 99%