Measurement of the fragmentation efficiency of rock mass blasting and its mining applications

Aler, J.; Mouza, J. du; Arnould, M.

doi:10.1016/0148-9062(95)00054-2

Cited by 80 publications

(34 citation statements)

References 2 publications

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“…Lu (1997) proposed models for the transition of the size distribution of the initial rock mass to the size distribution of the fragments after blasting. Alternative approaches include the use of Rock Engineering Systems (RES) to study the influence of each controlling parameter of the fragmentation phenomenon by blasting and predict the resultant fragment size distribution (Faramarzi et al 2013), multivariate analysis (Aler et al 1996, Chakraborty et al 2004, Monte Carlo simulations (Morin and Ficarazzo, 2006) or neural network and artificial intelligence methods (Kutilake et al 2010, Monjezi et al 2009, Saavedra et al 2006). …”

Section: Obtaining the Bsd From The Ibsdmentioning

confidence: 99%

“…Rock fragmentation is a topic that has been addressed in a variety of scientific disciplines, such as in structural geology interested in the fracture pattern of rocks as the result of the tectonic activity (Molnar et al 2007;Sammis and King, 2007); in fracture mechanics for the analysis of the response of cracks to both static and dynamic loading (Atkinson, 1987); in the assessment of the performance of the drilling equipment, mechanical excavators, tunnel boring machines (TBM), and for the consumption of disc cutters (Bakar et al 2014); and in the mining industry, interested in the size distribution of muck piles after blasting and on the subsequent crushing and grinding (Aler et al 1996;Morin et al 2006;Kulatilake et al 2010;Sanchidrián et al 2014). At rock mass instabilities the fragmentation of the detached rock mass has been acknowledged by several researchers either for rockfalls (Dussauge 2003, Giacomini et al 2009, Wang and Tonon 2010, Agliardi et al 2009, Crosta et al 2015 or for rock avalanches (Locat et al 2006;Crosta et al 2007;Davies et al 1999;Hewitt, 1998Hewitt, , 1999Hermanns et al 2006;McSaveney and Davis, 2007;Weidinger et al 2014).…”

Section: Rock Fragmentationmentioning

confidence: 99%

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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: Obtaining the Bsd From The Ibsdmentioning

confidence: 99%

Section: Rock Fragmentationmentioning

confidence: 99%

A fractal fragmentation model for rockfalls

2016

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“…A previous paper (Kulatilake et al 2010) has covered the literature on the topic to an extensive level by referring to the following papers: Ghosh et al (1990), Mojtabai et al (1990), Ouchterlony et al (1990), Chakraborty et al (1994), Pal Roy (1995), Hagan (1995), Aler et al (1996), Ozcelik (1998), Jhanwar et al (2000, Castro et al (1998), Latham and Lu (1999), Hamdi and Du Mouza (2005), Hall and Brunton (2002), Latham et al (2003), Sanchidrian et al (2007, Gheibie et al (2009), andRustan (1998). Kuznetsov (1973) has suggested the following empirical equation to predict the mean fragmentation size resulting from rock blasting:…”

Section: Literature Reviewmentioning

confidence: 98%

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Kulatilake

Hudaverdi

2012

Geotech Geol Eng

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The paper refers the reader to a blast data base developed in a previous study. The data base consists of blast design parameters, explosive parameters, modulus of elasticity and in situ block size. A hierarchical cluster analysis was used to separate the blast data into two different groups of similarity based on the intact rock stiffness. The group memberships were confirmed by the discriminant analysis. A part of this blast data was used to train a single-hidden layer back propagation neural network model to predict mean particle size resulting from blast fragmentation for each of the obtained similarity groups. The mean particle size was considered to be a function of seven independent parameters. An extensive analysis was performed to estimate the optimum value for the number of units for the hidden layer for each of the obtained similarity groups. The blast data that were not used for training were used to validate the trained neural network models. For the same two similarity groups, multivariate regression models were also developed to predict mean particle size. Capability of the developed neural network models as well as multivariate regression models was determined by comparing predictions with measured mean particle size values and predictions based on one of the most applied fragmentation prediction models appearing in the blasting literature. Prediction capability of the trained neural network models as well as multivariate regression models was found to be strong and better than the existing most applied fragmentation prediction model. Diversity of the blasts data used is one of the most important aspects of the developed models.

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“…Its main applications are the evaluation of the rock mass blockometry and the survey of the drainage of the fluid in the discontinuities network (Xu and Cojean 1990). It is based on the disk model, introduced by several other authors (Wang et al 1990, 1991, Wang 1992Aler and du Mouza 1996;Hamdi et al 2001). The 3D stochastic modelling of discontinuity networks using the SIMBLOC program is preceded by several tasks:…”

Section: A Brief Presentation Of Simblocmentioning

confidence: 99%

Fragmentation Energy in Rock Blasting

et al. 2007

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The theoretical explosive energy used in blasting is a common issue in many recent research works (Spathis 1999; Sanchidrian 2003). It is currently admitted that the theoretical available energy of the explosives is split into several parts during a blast: seismic, kinetic, backbreaks, heave, heat and fragmentation energies. Concerning this last one, the energy devoted to the breakage and to the creation of blocks within the muckpile can be separated from the microcracking energy which is devoted to developing new and/or extending existing micro cracks within the blocks (Hamdi et al. 2001;López et al. 2002). In order to investigate these two types of energy, a first and important task is to precisely study the main parameters characterising the two constitutive elements of the rock mass (rock matrix and discontinuity system). This should provide useful guidelines for the choice of the blasting parameters (type of explosive, blasting pattern, etc.), in order to finally control the comminution process. Within the frame of the EU LESS FINES research project, devoted to the control of fines production, the methodology was developed in order to: (1) characterize the in situ rock mass, by evaluating the density, anisotropy, interconnectivity and fractal dimension of the discontinuity system and (2) evaluate fragmentation (both micro and macro) energy spent during the blasting operation. The methodology was applied to three production blasts performed in the Klinthagen quarry (Sweden) allowing to estimate the part of the fragmentation energy devoted to the formation of muck pile blocks on one side and to the muckpile blocks microcracking on the other side.

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Measurement of the fragmentation efficiency of rock mass blasting and its mining applications

Cited by 80 publications

References 2 publications

A fractal fragmentation model for rockfalls

A fractal fragmentation model for rockfalls

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

Fragmentation Energy in Rock Blasting

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