An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

Saadat, Mahdi; Khandelwal, Manoj; Monjezi, Masoud

doi:10.1016/j.jrmge.2013.11.001

Cited by 138 publications

(39 citation statements)

References 16 publications

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“…These regulations are mainly based on peak particle velocity (PPV) due to blasting operations [3]. Many scientists have investigated the PPV [4][5][6][7]. The United States Bureau of Mines (USBM) proposed the first significant PPV predictor equation.…”

Section: Introductionmentioning

confidence: 99%

Ground Vibrations and Air Blast Effects Induced by Blasting in Open Pit Mines: Case of Metlaoui Mining Basin, Southwestern Tunisia

Aloui¹,

Bleuzen²

2016

J Geol Geosci

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Section: Introductionmentioning

confidence: 99%

Ground Vibrations and Air Blast Effects Induced by Blasting in Open Pit Mines: Case of Metlaoui Mining Basin, Southwestern Tunisia

Aloui¹,

Bleuzen²

2016

J Geol Geosci

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“…Some of the most important include those from USBM in 1959, Langefors-Kihlstrom in 1963, Ambraseys-Hendron in 1968, Nicholls et al in 1971, Siskind et al in 1980, Pal Roy in 1991and CMRI in 1993. These and other equations were listed by authors such as Khandelwal and Singh (2009), Kamali and Ataei (2010), Wahyudi et al (2011), Saadat et al (2014 and Kumar et al (2016).…”

Section: Blast-induced Ground Vibrationmentioning

confidence: 99%

“…Several algorithms can be used to train neural networks, but backpropagation-based algorithm (BP) has had preference among researchers to solve predicting problems (Wahyudi et al, 2011;Monjezi et al, 2013;Kamali andAtaei, 2010 andSaadat et al, 2014). Saadat et al (2014) have used, for function approximation, a feedforward ANN.…”

Section: Vibration Attenuation and Artificial Neural Network (Ann) Simentioning

confidence: 99%

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Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships

et al. 2018

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Blasting remains as an economical and reliable excavation technique, but there are some environmental shortcomings such as the control of blast-induced vibration. The impacts of vibration over surrounding communities in a blast area have been investigated for decades and researchers have been using a myriad of empirical predictive attenuation equations. These models, however, may not have satisfactory accuracy, since parameters associated to geomechanical properties and geology affect the propagation of seismic waves, making vibration modeling a complex process. This study aims for application of an Artificial Neural Network (ANN) method and Geomechanical parameter relationships to simulate the blast-induced vibration for a Brazilian mining site and then compare them to the traditional approach. ANN had the best performance for this mine despite having demanded large datasets (as much as for the traditional approach), while geomechanical parameters like RQD and GSI may be used to deliver a fair approach even without seismic data. Also, ANN methods may be useful in dealing with a large amount of information to facilitate the simulation process when combined with other methods. Therefore, alternative prediction methods may be helpful for small budget mining operations in planning and controlling blast-induced vibration and helping mining in urban areas becoming a more sustainable activity.

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“…Artificial intelligence has been diversely applied in earth sciences recently, using fuzzy logic (Demicco & Klir, 2004;Muhammad & Glass, 2011), neural networks (Bonaventura et al, 2017;Chatterjee et al, 2010;Izadi et al, 2017;Rogiers et al, 2012;Roslin & Esterle, 2016;Muhammad et al, 2014), and neuro-fuzzy modelling techniques (Cherkassky et al, 2006;Kar et al, 2014;Valdés & Bonham-Carter, 2006;Yegireddi & Uday Bhaskar, 2009;Yurdakul et al, 2014;Zoveidavianpoor et al, 2013). Recently, several researchers have solved backbreak problems through applying neural networks (Jang & Topal, 2013;Monjezi & Dehghani, 2008;Monjezi et al, 2013;Saadat et al, 2014;Sayadi et al, 2013;Ebrahimi et al, 2016), neuro-fuzzy techniques (Ghasemi et al, 2016), stochastic optimisation (Sari et al, 2013), and machine learning techniques (Khandelwal & Monjezi, 2012;Mohammadnejad et al, 2013). The findings differed according to rock type, as the characteristics of explosives and geometry of blast design are different in different conditions.…”

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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An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

Cited by 138 publications

References 16 publications

Ground Vibrations and Air Blast Effects Induced by Blasting in Open Pit Mines: Case of Metlaoui Mining Basin, Southwestern Tunisia

Ground Vibrations and Air Blast Effects Induced by Blasting in Open Pit Mines: Case of Metlaoui Mining Basin, Southwestern Tunisia

Comparing blast-induced ground vibration models using ANN and empirical geomechanical relationships

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

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