Evaluation of effect of blasting pattern parameters on back break using neural networks

Monjezi, Masoud; Dehghani, Hesam

doi:10.1016/j.ijrmms.2008.02.007

Cited by 157 publications

(62 citation statements)

References 14 publications

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“…These applications demonstrate that neural network models are efficient in solving problems when many parameters influence the process, and when the process is not fully understood. Monjezi et al (2008) have used the artificial neural network technique to determine the near-optimum blasting pattern so that back break can be minimized and found that the ratio of stemming to burden, the ratio of last row charge to total charge, powder factor, total charge per delay and the number of rows in a blasting round are the major causes of the back break problems. Li et al (2006) have proposed a coupling system of engineering database and three-layered back propogation neural networks for pre-splitting blasting design.…”

Section: Ann In Fragmentation Analysismentioning

confidence: 99%

Application of Artificial Neural Network for Blast Performance Evaluation

Trivedi¹

2014

IJRET

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Section: Ann In Fragmentation Analysismentioning

confidence: 99%

Application of Artificial Neural Network for Blast Performance Evaluation

Trivedi¹

2014

IJRET

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“…Also, the fact that the back-propagation algorithm is especially capable of solving predictive problems makes it so popular. The feed forward back-propagation neural network (BPNN) always consists of at least three layers; input layer, hidden layer and output layer (Monjezi & Dehghani, 2008). The number of hidden layers, as well as the number of neurons, in each hidden layer is dependent on the type of problem itself.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of neural networks for the prediction of rock fragmentation in Chadormalu iron mine / Zastosowanie sieci neuronowych do prognozowania stopnia rozdrobnienia skał w kopalni rud żelaza w Chadormalu

Monjezi¹,

Ahmadi²,

Khandelwal³

2012

Archives of Mining Sciences

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Most open-pit mining operations employ blasting for primary breakage of the in-situ rock mass. Inappropriate blasting techniques can result in excessive damage to the wall rock, decreasing stability and increasing water influx. In addition, it will result in either over and/or under breakage of rocks. The presence of over broken rocks can result in decreased wall stability and require additional excavation. In contrast, the presence of under broken rocks may require secondary blasting and additional crushing. Since blasting is a major cost factor, both cases (under and over breakage) create additional costs reflected in the increase of the operation and maintenance of the machinery. Quick and accurate measurements of fragment size distribution are essential for managing fragmented rock and other materials. Various fragmentation measurement techniques are available and are being used by industry/researchers but most of the methods are time consuming and not precise. An ideally performed blasting operation enormously influences the overall mining cost. This aim can be achieved by proper prediction and attenuation of fragmentation. Prediction of fragmentation is essential for optimizing blasting operation. Poor performance of the empirical models for predicting fragmentation has urged the application of new approaches. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation size distribution due to blasting in Chadormalu iron mine, Iran. In the development of the proposed ANN model, ten parameters such as UCS, drilling rate, water content, burden, spacing, stemming, hole diameter, bench height, powder factor and charge per delay were incorporated. Training and testing of the model was performed by the back-propagation algorithm using 97 datasets. A four-layer ANN was found to be optimum with architecture of 10-7-5-1. A comparison has made between measured results of fragmentation with predicted results of fragmentation by ANN and multiple regression model. Sensitivity analysis was also performed to understand the effect of each influencing parameters on rock fragmentation.Keywords: Fragmentation; blasting operation; ANN; Chadormalu iron mine W większości kopalń odkrywkowych stosuje się prace strzałowe w celu wstępnego rozbicia skał górotworu in situ. Niewłaściwe prowadzenie prac strzałowych spowodować może nadmierne uszkodzenie

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“…Backbreak, or rock breakage beyond the bounds of the last row in bench blasting (Olofsson, 1990), generally leads to pit wall angles smaller than those required, and may require costly artificial support techniques (Workman, 1992). It causes instability of rock slopes (Ashby, 1981), minimum drilling efficiency, and improper fragmentation (Monjezi & Dehghani, 2008). Wyllie & Mah (2004) suggested controlled blasting techniques such as cushion blasting, line drilling, and pre-shearing in order to minimise backbreak.…”

Section: Introductionmentioning

confidence: 99%

Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

Muhammad

Shah²

2017

Archives of Mining Sciences

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Backbreak, defined as excessive breakage behind the last row of blastholes in blasting operations at a quarry, causes destabilisation of rock slopes, improper fragmentation, minimises drilling efficiency. In this paper an artificial neural network (ANN) is applied to predict backbreak, using 12 input parameters representing various controllable factors, such as the characteristics of explosives and geometrical blast design, at the Dewan Cement limestone quarry in Hattar, Pakistan. This ANN was trained with several model architectures. The 12-2-1 ANN model was selected as the simplest model yielding the best result, with a reported correlation coefficient of 0.98 and 0.97 in the training and validation phases, respectively. Sensitivity analysis of the model suggested that backbreak can be reduced most effectively by reducing powder factor, blasthole inclination, and burden. Field tests were subsequently carried out in which these sensitive parameters were varied accordingly; as a result, backbreak was controlled and reduced from 8 m to less than a metre. The resulting reduction in powder factor (kg of explosives used per m 3 of blasted material) also reduced blasting costs.Keywords: Neural Network, backbreak, sensitivity analysis, modeling, blast design, quarry Kruszenie części złoża poza obszarem prowadzonych prac strzałowych oznacza nadmierne pęka-nie skał poza ostatnim rzędem otworów strzałowych w trakcie prac w kamieniołomach i prowadzi do destabilizacji górotworu poprzez zmianę nachylenia warstw skalnych, powoduje niepotrzebną fragmentację skał i obniża efektywność prac wiertniczych. W pracy tej wykorzystano sztuczną sieć neuronową (ANN) do przewidywania zasięgu kruszenia dalszej części złoża przy wykorzystaniu 12 parametrów wejściowych. Parametry te opisują różne zmienne czynniki, np. charakterystyka materiału wybuchowego czy przyjęty plan prac strzałowych w kamieniołomie Deewan w regionie Hattar w Pakistanie. Prowadzono proces uczenia sieci dla różnej architektury modelu, wybrano model 12-2-1 ANN, jako model najprostszy, zapewniający najlepszy wynik a współczynniki korelacji uzyskane dla fazy uczenia i walidacji wyniosły odpowiednio 0.98 i 0.97. Przeprowadzona analiza wrażliwości modelu wykazała że zasięg kruszenia dalszych części złoża obniżyć można poprzez zmianę parametrów ładunku strzelniczego, zmianę nachylenia otworów strzałowych oraz zmianę przybitki. Badania terenowe w czasie których ulegały zmianie wartości wyżej wymienionych wrażliwych parametrów wykazały, że zasięg kruszenia złoża poza obszarem prac strzałowych ograniczono z uprzednich 8 m do wielkości poniżej jednego metra. Obniżenie współczynnika charakteryzującego ładunek (kg zastosowanego materiału wybuchowego przypadający na 1 m 3 rozkruszonego materiału skalnego) pozwoliło także na obniżenie kosztów prac strzałowych.Słowa kluczowe: sieci neuronowe, zasięg kruszenia, analiza wrażliwości, modelowanie, projektowanie robót strzałowych, kamieniołom

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Evaluation of effect of blasting pattern parameters on back break using neural networks

Cited by 157 publications

References 14 publications

Application of Artificial Neural Network for Blast Performance Evaluation

Application of Artificial Neural Network for Blast Performance Evaluation

Application of neural networks for the prediction of rock fragmentation in Chadormalu iron mine / Zastosowanie sieci neuronowych do prognozowania stopnia rozdrobnienia skał w kopalni rud żelaza w Chadormalu

Minimising Backbreak at the Dewan Cement Limestone Quarry Using an Artificial Neural Network

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