Artificial neural network as a tool for backbreak prediction
- Authors: Monjezi, Masoud , Hashemi Rizi, S , Majd, Vahdi , Khandelwal, Manoj
- Date: 2014
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 32, no. 1 (2014), p. 21-30
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- Description: Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R2 = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively. © 2013 Springer Science+Business Media Dordrecht.
A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak
- Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
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- Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
Evaluation of effect of blast design parameters on flyrock using artificial neural networks
- Authors: Monjezi, Masoud , Mehrdanesh, Amirhossein , Malek, Alaeddin , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 23, no. 2 (2013), p. 349-356
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- Description: Flyrock, the propelled rock fragments beyond a specific limit, can be considered as one of the most crucial and hazardous events in the open pit blasting operations. Involvement of various effective parameters has made the problem so complicated, and the available empirical methods are not proficient to predict the flyrock. To achieve more accurate results, employment of new approaches, such as artificial neural network (ANN) can be very helpful. In this paper, an attempt has been made to apply the ANN method to predict the flyrock in the blasting operations of Sungun copper mine, Iran. Number of ANN models was tried using various permutation and combinations, and it was observed that a model trained with back-propagation algorithm having 9-5-2-1 architecture is the best optimum. Flyrock were also computed from various available empirical models suggested by Lundborg. Statistical modeling has also been done to compare the prediction capability of ANN over other methods. Comparison of the results showed absolute superiority of the ANN modeling over the empirical as well as statistical models. Sensitivity analysis was also performed to identify the most influential inputs on the output results. It was observed that powder factor, hole diameter, stemming and charge per delay are the most effective parameters on the flyrock. © 2012 Springer-Verlag London Limited.
Application of soft computing in predicting rock fragmentation to reduce environmental blasting side effects
- Authors: Monjezi, Masoud , Mohamadi, Hasan , Barati, Bahare , Khandelwal, Manoj
- Date: 2014
- Type: Text , Journal article
- Relation: Arabian Journal of Geosciences Vol. 7, no. 2 (2014), p. 505-511
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- Description: In the blasting operation, risk of facing with undesirable environmental phenomena such as ground vibration, air blast, and flyrock is very high. Blasting pattern should properly be designed to achieve better fragmentation to guarantee the successfulness of the process. A good fragmentation means that the explosive energy has been applied in a right direction. However, many studies indicate that only 20-30 % of the available energy is actually utilized for rock fragmentation. Involvement of various effective parameters has made the problem complicated, advocating application of new approaches such as artificial intelligence-based techniques. In this paper, artificial neural network (ANN) method is used to predict rock fragmentation in the blasting operation of the Sungun copper mine, Iran. The predictive model is developed using eight and three input and output parameters, respectively. Trying various types of the networks, it was found that a trained model with back-propagation algorithm having architecture 8-15-8-3 is the optimum network. Also, performance comparison of the ANN modeling with that of the statistical method was confirmed robustness of the neural networks to predict rock fragmentation in the blasting operation. Finally, sensitivity analysis showed that the most influential parameters on fragmentation are powder factor, burden, and bench height. © 2012 Saudi Society for Geosciences.
Backbreak prediction in the Chadormalu iron mine using artificial neural network
- Authors: Monjezi, Masoud , Ahmadi, Zabiholla , Yazdian-Varjani, Ali , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 23, no. 3-4 (2013), p. 1101-1107
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- Description: Backbreak is one of the unfavorable blasting results, which can be defined as the unwanted rock breakage behind the last row of blast holes. Blast pattern parameters, like stemming, burden, delay timing, stiffness ratio (bench height/burden) and rock mass conditions (e.g., geo-mechanical properties and joints), are effective in backbreak intensity. Till date, with the exception of some qualitative guidelines, no specific method has been developed for predicting the phenomenon. In this paper, an effort has been made to apply artificial neural networks (ANNs) for predicting backbreak in the blasting operation of the Chadormalu iron mine (Iran). Number of ANN models with different hidden layers and neurons were tried, and it was found that a network with architecture 10-7-7-1 is the optimum model. A comparative study also approved the superiority of the ANN modeling over the conventional regression analysis. Mean square error (MSE), variance account for (VAF) and coefficient of determination (R 2) between measured and predicted backbreak for the ANN model were calculated and found 89.46 %, 0.714 and 90.02 %, respectively. Also, for the regression model, MSE, VAF and R 2 were computed and found 66.93 %, 1.46 and 68.10 %, respectively. Sensitivity analysis was also carried out to find out the influence of each input parameter on backbreak results, and it was revealed that burden is the most influencing parameter on the backbreak, whereas water content is the least effective parameter in this regard. © 2012 Springer-Verlag London Limited.
Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network
- Authors: Monjezi, Masoud , Hasanipanah, Mahdi , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 22, no. 7-8 (2013), p. 1637-1643
- Full Text: false
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- Description: The purpose of this article is to evaluate and predict blast-induced ground vibration at Shur River Dam in Iran using different empirical vibration predictors and artificial neural network (ANN) model. Ground vibration is a seismic wave that spreads out from the blasthole when explosive charge is detonated in a confined manner. Ground vibrations were recorded and monitored in and around the Shur River Dam, Iran, at different vulnerable and strategic locations. A total of 20 blast vibration records were monitored, out of which 16 data sets were used for training of the ANN model as well as determining site constants of various vibration predictors. The rest of the 4 blast vibration data sets were used for the validation and comparison of the result of ANN and different empirical predictors. Performances of the different predictor models were assessed using standard statistical evaluation criteria. Finally, it was found that the ANN model is more accurate as compared to the various empirical models available. As such, a high conformity (R 2 = 0.927) was observed between the measured and predicted peak particle velocity by the developed ANN model. © 2012 Springer-Verlag London Limited.