A Method to Estimate In Situ Block Size Distribution

Elmouttie, Marc; Poropat, George

doi:10.1007/s00603-011-0175-0

Cited by 58 publications

(33 citation statements)

References 23 publications

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“…The lack of accessibility often forces the measurements being collected at the bottom of the rock wall, which may not represent the fracture pattern at the source. This issue has been discussed in the literature and several procedures have been proposed to overcome it (Da Gama, 1977: Miles, 1972Hudson and Priest, 1979;Lu and Latham, 1999;Kalenchuk et al 2006;Stavropoulou, 2014;Elmouttie and Poropat, 2012). The common idea is to use the field measurements of the joints set attributes to develop algorithms which generate stochastically combinations of the joint sets that follow the statistical distributions fitted in the real field measurements.…”

Section: Generation Of the Ibsdmentioning

confidence: 99%

A fractal fragmentation model for rockfalls

2016

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(150-250 words)The impact-induced rock mass fragmentation in a rockfall is analyzed by comparing the In Situ Block Size Distribution (IBSD) of the rock mass detached from the cliff face and the resultant Rockfall Block Size Distribution (RBSD) of the rockfall fragments on the slope. The analysis of several inventoried rockfall events suggests that the volumes of the rockfall fragments can be characterized by a power law distribution. We propose the application of a three-parameter Rockfall Fractal Fragmentation Model (RFFM) for the transformation of the IBSD into the RBSD. A Discrete Fracture Network model is used to simulate the discontinuity pattern of the detached rock mass and to generate the IBSD. Each block of the IBSD of the detached rock mass is an initiator. A survival rate is included to express the proportion of the unbroken blocks after the impact on the ground surface. The model was calibrated using the volume distribution of a rockfall event in Vilanova de Banat in the Cadí Sierra, Eastern Pyrenees, Spain. The RBSD was obtained directly in the field, by measuring the rock blocks fragments deposited on the slope. The IBSD and the RBSD were fitted by exponential and power-law functions, respectively. The results show that the proposed fractal model can successfully generate the RBSD from the IBSD and indicate the model parameter values for the case study.

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Section: Generation Of the Ibsdmentioning

confidence: 99%

A fractal fragmentation model for rockfalls

2016

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“…It is, however, relatively easy to statistically quantify rock size. The size distribution of detached rocks can be approximated by performing field measurements of the in situ block size distribution (e.g., Sturzenegger et al 2011;Elmouttie and Poropat 2012) or the size distribution of rockfall blocks from past events that have come to rest on the slope (e.g., Fityus et al 2013). Despite this, size distributions of rocks are rarely used in the modeling of rockfall trajectories.…”

Section: Systematic Probabilistic Modelingmentioning

confidence: 99%

Probabilistic modeling of rockfall trajectories: a review

Lan

2015

Bull Eng Geol Environ

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Probabilistic approaches are widely adopted for the modeling of rockfall trajectories, but are not widely discussed in the literature. This paper aims to help fill this gap by reviewing probabilistic models of rockfall trajectories, while providing some perspectives for future study. We first make it clear that, from a theoretical point of view, the probabilistic approach is necessitated by both the ontic (inherent) uncertainty associated with rockfalls and the epistemic (information) uncertainty associated with numerical modeling. The review suggests that there may be the potential to improve the probabilistic modeling of rockfall trajectories in various aspects, including the systematic probabilistic modeling criterion, the random sampling approaches employed for probabilistic variables, the probabilistic modeling of rock shape, and the probabilistic prediction of rockfall intensity. However, there are still some open questions regarding the promotion of probabilistic modeling in practice. It is not clear whether probabilistically treating all of the variables of rockfall trajectory model with reasoned distributions will lead to significantly improved results, or whether the improvements will be great enough (given the difficulties and costs involved) that it is worth quantifying all of the uncertainties involved in rockfall trajectory modeling. The answers to these questions can be found in the practice of probabilistic modeling itself.

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“…They include the modelling of discontinuity surveys and the computer determination of the blocks dissected by discontinuities [10]; the use of a three-dimensional model for predicting the characteristics of ore fragmentation in block caving [11]; the use of a termed-orthogonal model in which the block is defined by joint spacing in three predetermined directions [12]; and [13] a technique based on Monte Carlo simulations that uses a more realistic fracture geometry than other block size estimation previous methods.…”

Section: Related Workmentioning

confidence: 99%