Numerical analyses of stability and deformation behavior of reinforced and unreinforced tunnel faces

Paternesi, Alessandra; Schweiger, Helmut; Scarpelli, Giuseppe

doi:10.1016/j.compgeo.2017.04.002

Cited by 69 publications

(21 citation statements)

References 15 publications

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“…The optimal values of µ and σ can be determined by equating the gradients of the above equation with respect to µ and σ, respectively, to zero, as shown in Equations (17) and (18).…”

Section: Estimation Of Model Parametersmentioning

confidence: 99%

“…The existing methods that have been used for evaluations of tunnel face stability include limit equilibrium [15], limit analysis [16,17], the displacement finite-element method [18], the discrete numerical method [19], scale model test [20][21][22], and centrifuge model test [23][24][25]. These methods have been proven to be useful in determining the limit support pressure and evaluating the effects of reinforcement measures.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Tunnel Face Stability Using a Naive Bayes Classifier

2019

Applied Sciences

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This paper develops a convenient approach for facilitating the prediction of tunnel face stability in the framework of Bayesian theorem. First, a number of values of the features influencing the face-stability of tunnels are chosen according to the full factorial design. Secondly, the software OptumG2 is utilized to performed strength reduction analyses to obtain safety factors regarding tunnel face stability. Based on the simulated safety factors, the chosen samples are labeled as stable (Fs≥1) or unstable samples (Fs<1). Thirdly, the model parameters that characterize the distribution of the random variables are then estimated by maximizing the well-known likelihood function. After that, the probability density functions (PDF) of the features are identified, and a naive Bayes classifier is constructed with the prior probabilities of the stable and the unstable state. The so-called type І and type І І errors are estimated with stable and unstable samples, respectively. The model parameters are then calibrated with additional stable samples to obtain the second classifier. Finally, the two classifiers are evaluated using independent samples that have not been seen in the training dataset. The proposed method allows geotechnical engineers to predict the stability of tunnel faces with great efficiency. It is applicable for general cases of tunnels where the parameters are within the ranges bounded by the specified values.

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“…The optimal values of µ and σ can be determined by equating the gradients of the above equation with respect to µ and σ, respectively, to zero, as shown in Equations (17) and (18).…”

Section: Estimation Of Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Tunnel Face Stability Using a Naive Bayes Classifier

2019

Applied Sciences

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“…The magnitude of deformations around circular tunnels are very much different according to the applied tensile stress, materials properties (hard/soft rocks and soil) and the dimension 400 of tunnel. If the tunnel depths are limited to very shallow (< 10-20 m), the deformation values calculated in the case studies vary from sub-millimetre depending on the scenarios (Paternesi et al, 2017;Abdellah et al, 2018;Zhang and Li, 2009).…”

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confidence: 99%

Quaternary lava tubes distribution in Jeju Island and their potential deformation risks

Kim

Lin

Oh³

2020

Preprint

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Abstract. Jeju Island, located in the southern sea of the Korean Peninsula, is a volcanic island created by the tertiary and quaternary volcanic eruptions. A group of lava tubes formed between quaternary lava flows constitutes one of the most predominant geological contexts owing to its unique and complex network, for which a total of 178 lava tubes is estimated. As a significant portion of lava caves have not been discovered, the threat caused by lava cave collapse has become one of the major concerns in connection with the recent infrastructure construction in Jeju Island. Considering the risk potential, the overall distribution and collapsing risk of the Jeju lava tube network were investigated in this study. Through spatial analysis, we firstly found that the lava tubes distribution is not correlated with specific geological units. Secondly, the risk of collapse is high especially when there are ongoing artificial constructions around the undisclosed lava tube network. We therefore introduced Interferometric Synthetic Aperture Radar (InSAR) techniques to measure the deformation of the ground surface where lava tube networks distributed underground. InSAR results and the proposed machine learning applications identified that the populations of ground deformations was up to 1–2 mm/year and was inferred to be caused by the instability of the shallow lava cavity. Given that underground cavities could pose serious risks, a detailed physical exploration and threat assessment of potential cave groups is required before intensive anthropogenic construction is developed.

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“…Despite the simple isotropic formulation, its applicability was proved in various numerical simulations in geotechnics, e.g. [4,5,19,20,22,33,41]. The extended HSS model is also implemented in commercial geotechnical FE codes available for researchers and practitioners.…”

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confidence: 99%

Refinement of the Hardening Soil model within the small strain range

Cudny

Truty

2020

Acta Geotech.

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The popularity of the elasto-plastic Hardening Soil (HS) model is based on simple parameter identification from standard testing and empirical formulas. The HS model is implemented in many commercial FE codes designed to analyse geotechnical problems. In its basic version, the stress-strain behaviour within the elastic range is subject to the hypoelastic power law, which assures the barotropy of the elastic stiffness. However, a proper modelling within the small strain range, i.e. strain-induced stiffness degradation and correct reproduction of the hysteretic behaviour, was one of the most important drawbacks in the HS formulation. The first small strain stiffness extension to the HS model was proposed by Benz (Small strain stiffness of soils and its numerical consequences, 2007), and the new model was called Hardening Soil Small (HSS). Despite the simple isotropic formulation, its applicability was proved in various numerical simulations in geotechnics. However, the HSS formulation exhibits a serious fault known in the literature as overshooting, i.e. uncontrolled reset of the loading memory after tiny unloading-reloading cycles. The authors' main aim was to retain the set of material parameters for the HSS formulation and to propose a new small strain extension to the HS model without overshooting. The new proposal is based on the BRICK model which represents the concept of nested yield surfaces in strain space. The implementation aspects of the new HS-Brick model are described, and its performance is presented in some element tests and selected boundary value problems by comparisons with the HSS formulation.

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Numerical analyses of stability and deformation behavior of reinforced and unreinforced tunnel faces

Cited by 69 publications

References 15 publications

Prediction of Tunnel Face Stability Using a Naive Bayes Classifier

Prediction of Tunnel Face Stability Using a Naive Bayes Classifier

Quaternary lava tubes distribution in Jeju Island and their potential deformation risks

Refinement of the Hardening Soil model within the small strain range

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