Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines

Ghasemi, Ebrahim; Sarı, Mehmet; Ataei, Mohammad

doi:10.1016/j.ijrmms.2012.03.011

Cited by 136 publications

(56 citation statements)

References 18 publications

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“…The most important controllable factors are incompetent stemming, inappropriate burden and spacing, inaccurate drilling, too much explosive energy, inadequate delay timing and unwarranted powder factor [1,16,17]. In the case of uncontrollable factors, the most effective factors are related to the rock mass properties.…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

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Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

View full text Add to dashboard Cite

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“…Conducting a desired blasting operation may provide several beneficial factors, such as, overall cost reduction and improving the efficiency of drilling operation (Bhandari 1997;Faradonbeh et al 2015). According to several scholars, more than 85 % of blasting energy is dissipated through the ground and can create some undesirable environmental effects on surrounding areas (Berta 1990;Khandelwal and Singh 2007;Ghasemi et al 2012;Monjezi et al 2013;Khandelwal and Monjezi 2013a;Hajihassani et al 2014;Jahed Armaghani et al 2015a;Ebrahimi et al 2015;Hasanipanah et al 2015a, b). Flyrock is one of these environmental issues and is defined as any unexpected throw or movement of the fragmented rocks due to excessive force of the explosive material (Bhandari 1997;IME 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Bajpayee et al (2004) mentioned that any mismatch between the highlighted factors can generate flyrock phenomenon. According to Little and Blair (2010) and Ghasemi et al (2012), there are three creation mechanisms for flyrock: cratering, rifling, and face bursting which are explained as follows.…”

Section: Introductionmentioning

confidence: 99%

“…If the utilized stemming material is inefficient or is absent, rifling will be occurred. During rifling, gases from blast can stream up the blast hole along the path with the least resistance resulting in stemming ejection and sometimes ejection of the collar rock as harmful flyrock (Ghasemi et al 2012). Face bursting happens when explosive material converges major geological structures or when it is situated in close closeness of fragile planes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

et al. 2016

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Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.

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“…In order to achieve a reasonable fly rock distance for providing safety in mine, decrease of fly rock is one of the main worries of the blasting designers Bajpayee et al, 2002Bajpayee et al, , 2003Bajpayee et al, , 2004aBajpayee et al, , 2004bGhasemi et al, 2012aGhasemi et al, , 2012bKecojevic & Radomsky, 2005;Little & Blair, 2010;Monjezi et al, 2012;Ning, 1999;Rehak et al, 2001;Rezaei et al, 2011;Stojadinović et al, 2011;Tota et al, 2001). In some cases that ground vibration and air blasting has caused some problems in mines designer should predict these phenomena and redesign blasting pattern (Ak et al, 2009;Bakhshandeh Amnieh et al, 2012;Dehghani & Ataee-Pour, 2011;Guosheng et al, 2011;Hudaverdi, 2012;Iphar et al, 2008;Monjezi et al, 2010aShuran & Shujin, 2011).…”

Section: Introductionmentioning

confidence: 99%

Selection of Most Proper Blasting Pattern in Mines Using Linear Assignment Method: Sungun Copper Mine / Wybór Najodpowiedniejszego Schematu Prowadzenia Prac Strzałowych W Kopalni Miedzi Sungun Z Użyciem Metody Przyporządkowania Liniowego

Yari¹,

Bagherpour²,

Jamali³

et al. 2015

Archives of Mining Sciences

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One of the most important operations in mining is blasting. Improper design of blasting pattern will cause technical and safety problems. Considering impact of results of blasting on next steps of mining, correct pattern selection needs a great cautiousness. In selecting of blasting pattern, technical, economical and safety aspects should be considered. Thus, most appropriate pattern selection can be defined as a Multi Attribute Decision Making (MADM) problem. Linear assignment method is one of the very applicable methods in decision making problems. In this paper, this method was used for the first time to evaluate blasting patterns in mine. In this ranking, safety and technical parameters have been considered to evaluate blasting patterns. Finally, blasting pattern with burden of 3.5 m, spacing of 4.5 m, stemming of 3.8 m and hole length of 12.1 m has been presented as the most suitable pattern obtained from linear assignment model for Sungun Copper Mine.

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Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines

Cited by 136 publications

References 18 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting

Selection of Most Proper Blasting Pattern in Mines Using Linear Assignment Method: Sungun Copper Mine / Wybór Najodpowiedniejszego Schematu Prowadzenia Prac Strzałowych W Kopalni Miedzi Sungun Z Użyciem Metody Przyporządkowania Liniowego

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