Safe rapid drifting – Support selection

He, Qingyuan; Kaiser, Peter K.; Henning, John G.

doi:10.1016/j.tust.2008.01.002

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…This technique involves an excavation or work cycle comprising a number of different activities. Suorineni et al [3] mention the following unit operations: drilling of the tunnel surface, loading of explosives and blasting, ventilation (considered as an interference within the cycle), scaling and loading of the blasted material, and fortification (bolts, nets, and shotcrete, among others). Figure 1 illustrates the drilling and blasting cycles involved in tunneling.…”

Section: Tunnel Constructionmentioning

confidence: 99%

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Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC)

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et al. 2015

Mathematical Problems in Engineering

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Tunnels, drifts, drives, and other types of underground excavation are very common in mining as well as in the construction of roads, railways, dams, and other civil engineering projects. Planning is essential to the success of tunnel excavation, and construction time is one of the most important factors to be taken into account. This paper proposes a simulation algorithm based on a stochastic numerical method, the Markov chain Monte Carlo method, that can provide the best estimate of the opening excavation times for the classic method of drilling and blasting. Taking account of technical considerations that affect the tunnel excavation cycle, the simulation is developed through a computational algorithm. Using the Markov chain Monte Carlo method, the unit operations involved in the underground excavation cycle are identified and assigned probability distributions that, with random number input, make it possible to simulate the total excavation time. The results obtained with this method are compared with a real case of tunneling excavation. By incorporating variability in the planning, it is possible to determine with greater certainty the ranges over which the execution times of the unit operations fluctuate. In addition, the financial risks associated with planning errors can be reduced and the exploitation of resources maximized.

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Section: Tunnel Constructionmentioning

confidence: 99%

“…In the first loop, there is the variable "operation," whose purpose is to identify the operations that will be performed in the third loop and that are adapted according to the operations of the construction cycle described by Suorineni et al [3]. It should be noted that this is the only alphanumeric variable.…”

Section: Control Of the Number Of Simulationsmentioning

confidence: 99%

Planning Tunnel Construction Using Markov Chain Monte Carlo (MCMC)

Vargas

Koppe

Pérez

et al. 2015

Mathematical Problems in Engineering

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“…Therefore, blast-induced damage is not accounted for in the modified Q 0 in Equation (1). Suorineni et al [55] argue that excavation-induced damage in rock masses should be taken into account in estimating rock mass quality for support selection. This argument is supported by Løset [52] and Hoek et al [56].…”

Section: Blasting Effectsmentioning

confidence: 99%

The stability graph after three decades in use: Experiences and the way forward

2010

International Journal of Mining, Reclamation and Environment

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The stability graph was introduced approximately three decades ago for open stope design. Since then, it has gained wide recognition in open stope design around the world in hard rock metalliferous mining. Several developments have taken place following its initial inception aimed at improving its reliability in predicting the stability state of open stopes. These developments include redefinition of the stability graph number factors, the transition zones and addition of new factors. Various types of stability graphs have also emerged over the years for other purposes such as cablebolt layout design. The original database has also been significantly expanded from the 26 cases in 1981 to 483 cases to date. This article critically reviews the developments of the stability graph to date with the objective of: (1) Synthesising the scattered knowledge of these developments in the literature to a single source. (2) Creating awareness among potential users of the method, of the problems and risks arising from the uncoordinated developments in the method and the consequences of the lack of consensus in the choice of stability number factors. (3) Identifying areas for further research to improve the reliability of the method. (4) Finally, providing guidelines to inexperienced users and practitioners unaware of the various developments on the stability graph on when to use any one of the several stability graph types currently available. The article stresses that as an empirical method, the reliability of the stability graph method is largely dependent on the size, quality and consistency of the database. Hence, there must be consistency in the determination of the stability graph factors and accepted stope stability state transition zones. The present tendency for authors to arbitrarily choose between the original and modified stability number factors result in incomparable data that cannot be combined, while the different transition zones result in different interpretations of the stability state of stopes. The review also shows that there is need for factors that account for stope stand-up time, blast damage and gravity factor that is stress factor dependent. There is also a need to develop procedures for determining stability of open stope surfaces that are made of backfill. The inexperienced user and practitioner unaware of the various versions of the stability graph should be conscious of the different versions and types of stability graphs to make the appropriate choice for his/her design. The stability graph should also be used with caution, when applied to narrow vein orebodies because no version of the graphs accounts for orebody thickness in the definitions of the stability states.

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“…There are many factors affecting construction time of a drill and blast tunnel (Chen and Liu, 2007;Palmstrom and Brock, 2006;Suorineni et al, 2008;Zare and Bruland, 2007). Hence, it is very difficult to predict and analyze how much each factor influences the total construction time, even though it is important for efficient work scheduling.…”

Section: Introductionmentioning

confidence: 99%