Parametric study to develop guidelines for blast fragmentation improvement in jointed and massive formations

Chakraborty, A.K.; Raina, A.K.; Ramulu, M.; Choudhury, Prasenjit; Haldar, A.; Sahu, P.B.; Bandopadhyay, C.

doi:10.1016/j.enggeo.2003.12.003

Cited by 48 publications

(26 citation statements)

References 4 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%

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%

A fractal fragmentation model for rockfalls

2016

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“…Such variables have been discussed by many authors (Lu & Latham, 1998;Thornton et al, 2002;Mancini & Cardu, 2001;Chakraborty et al, 2004. ) following a general classification that includes: I) geometrical variables (drilling pattern and bench geometry); II) explosive variables; III) initiation system variables; and IV) rock parameters.…”

Section: Complexity Analysismentioning

confidence: 99%

“…One important challenge is the high degree of uncertainty posed by local geological conditions. In practical situations, due to structural conditions (Grenon et al, 1998;Lu & Latham, 1998, Chakraborty et al, 2004, the cost curves, and hence the optimum conditions, vary from bench to bench. Nevertheless, aiming at industrial level productivity, a robust pre-defined blast design must be used to drive the subsequent operations, with minimal adjustments if any.…”

Section: Introductionmentioning

confidence: 99%

Gestão de fragmentação no desmonte de rochas

Seccatore

Tomi

Munaretti

et al. 2011

Rem: Rev. Esc. Minas

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ResumoEsse trabalho descreve a pesquisa sobre a aplicação de análise de complexidade para avaliar o desempenho do processo de desmonte de rocha na mineração. O objetivo principal da pesquisa é o desenvolvimento de um método que permita identificar uma solução robusta para gerenciar a fragmentação do desmonte e para planejar o processo de forma a alcançar as metas de produtividade da britagem. A pesquisa utiliza ferramentas de análise de complexidade, introduzindo uma abordagem particular de sistemas complexos inédita em mineração e em aplicações de desmonte. Os resultados alcançados indicam que a compreensão adequada das correlações entre as variáveis-chaves no desmonte traz benefícios importantes para apoiar a tomada de decisão no ambiente de alta incerteza do processo de desmonte de rocha. A busca por um sistema robusto se mostrou mais relevante do que a busca por uma solução teoricamente ótima, já que os níveis de robustez e de complexidade podem ser utilizados para comparar diferentes modelos e para medir a aderência desses modelos aos dados de campo. Palavras-chave:Perfuração e desmonte; fragmentação de rocha; análise de complexidade, análise de robustez. Abstract The paper describes the research carried out in the application of complexity analysis in order to

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“…Suitable and uniform particle size distribution results increase in crusher and mill throughput and decrease in energy consumption in size reduction process. Mckenzie (1966) found, in the studies at Quebec Cartier Mines, that the efficiency of all the subsystems of mining is dependent on the fragmentation (Chakraborty et al 2004). Today, researchers suggest 'mine to mill' blasting approach that is defined as optimization of the blast design to maximize the overall profitability rather than individual operations (Kanchibotla et al 1999;Grundstrom et al 2001).…”

Section: Introductionmentioning

confidence: 97%

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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Parametric study to develop guidelines for blast fragmentation improvement in jointed and massive formations

Cited by 48 publications

References 4 publications

A fractal fragmentation model for rockfalls

A fractal fragmentation model for rockfalls

Gestão de fragmentação no desmonte de rochas

New Prediction Models for Mean Particle Size in Rock Blast Fragmentation

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