The use of stress-strength relationships in the assessment of tunnel stability

Bhasin, Rajinder; Grimstad, E.

doi:10.1016/0886-7798(95)00047-x

Cited by 98 publications

(38 citation statements)

References 4 publications

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“…# Jethwa et al (1984) N c = σ cm /γH ≤ 2.0; σ cm = 2c p cosϕ p /(1 − sinϕ p ) σ cm , γ, and H -Semi-empirical (1) Singh et al (1992) H ≥ 350Q 1/3 H and Q 39 case histories Empirical (2) Goel et al (1995) H ≥ 270 N 0.33 · B −0.1 with N = (Q) SRF = 1 H, N, and B 72 cases histories Empirical (3) Aydan et al (1993) α = σ c /γH ≤ 2.0 σ c , γ, and H Cases from Japan tunnels Semi-empirical (4) Barla (1995) σ cm /γH ≤ 1.0 σ cm , γ, and H -Semi-empirical (5) Bhasin and Grimstad (1996) σ θ /σ cm ≥ 1.0 with σ cm = 0.7γQ 1/3 σ cm and σ θ Tunnel case histories Semi-empirical (6) Hoek and Marinos (2000) ε = (0.002-0.0025p i /p 0 ) · (σ cm /p 0 ) (2.4p…”

Section: Proposed Bymentioning

confidence: 99%

Predicting tunnel squeezing with incomplete data using Bayesian networks

Feng

Jimenez

2015

Engineering Geology

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Section: Proposed Bymentioning

confidence: 99%

Predicting tunnel squeezing with incomplete data using Bayesian networks

Feng

Jimenez

2015

Engineering Geology

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“…(b) Stiffness of steel sets e effective stiffness of a steel set with backfill, K sb , can be estimated as [16] H and Q irty-nine case histories Empirical Aydan et al [12] α � σ c /cH ≤ 2.0 σ c , c, and H Cases from Japan tunnels Semiempirical Barla [13] σ cm /cH ≤ 1.0 σ cm , c, and H -Semiempirical Bhasin and Grimstad [14] σ θ /σ cm ≥ 1.0 with σ cm � 0.7cQ …”

Section: Database Descriptionmentioning

confidence: 99%

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun

Feng

Yang

2018

Advances in Civil Engineering

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Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. erefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. is study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. e proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

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“…Using the average value of q (= 0.7) and the E m /E r versus RQD relation in Eq. (2c) [36], the new formula is the following: Fig. 9.…”

Section: Q Based Methodsmentioning

confidence: 99%

“…using Eqs. (42) and (43): This equation corresponds to the Hoek's suggested one, which is published in the paper of Zhang [36]:…”

Section: Yudhbir Et Al Was the First At 1983 [30] Who Suggested The mentioning

confidence: 99%

Empirical methods of calculating the mechanical parameters of the rock mass

Vásárhelyi

Kovács²

2016

Period. Polytech. Civil Eng.

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The knowledge of the main mechanical constants of a rock mass (such as strength, deformability and the Poisson's ratio) is one of the most important for rock engineering design on or in rock mass. Until now, several empirical relationships were determined for calculating these material constants based on both the quality of the studied rock mass (ie. RMR or GSI values) IntroductionLarge scale rock mass characterization introduces material parameters related to mechanical properties. The most important properties are the deformation modulus; the unconfined strength and the Poisson's ratio value of the rock mass in interest. These material parameters are frequently related to laboratory data characteristics of intact rock samples and to the classical rock mass classification systems (e.g. RQD, Q, RMR or GSI). These rock mass quality measures quantify the relation between the rock mass and the intact rock.The established empirical relations between the mechanical parameters of rock masses (unconfined compressive strength, deformation modulus) and the rock mass classification systems (RMR or GSI values) show exponential increasing deformation modulus and compressive strength with the increasing quality of the rock mass.The paper summarizes the observed correlations published between the mechanical properties of rock masses and one of the rock mass classification systems. It was found, that the deformation modulus and the strength of a rock mass data may reflect a simple exponential relationship of the observed quantities. According to analysis of different proposed equations a new formula is suggested and the modification ratio (i.e. ratio of the deformation modulus and the strength of rock mass) is also determined.However, it is important to note, that there are huge differences between the published date and the empirical formulas. The reason for this variance is the difference in situ testing methods, that may give different values of mechanical parameters even for the same rock mass. According to Bieniawski [1], even a single testing method, such as flat jack test, can lead to a widely scattering results even where the rock mass is very uniform. The other reason for the discrepancy in the different calculation methods is the directional effect. Most rock masses are anisotropic and do not have single deformation modulus [2].There is no mechanical (physical) interpretation of the above empirical equations but these were analysed by e.g. [3]. Recently, Ván and Vásárhelyi [4] suggested a damage mechanical approach to analyse them. In this paper their method will be also presented.

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The use of stress-strength relationships in the assessment of tunnel stability

Cited by 98 publications

References 4 publications

Predicting tunnel squeezing with incomplete data using Bayesian networks

Predicting tunnel squeezing with incomplete data using Bayesian networks

Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Empirical methods of calculating the mechanical parameters of the rock mass

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