Showing items 1 - 2 of 2

Evaluation of effect of blast design parameters on flyrock using artificial neural networks

- Monjezi, Masoud, Mehrdanesh, Amirhossein, Malek, Alaeddin, Khandelwal, Manoj

  • 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.

Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

- Rezaeineshat, Afsaneh, Monjezi, Masoud, Mehrdanesh, Amirhossein, Khandelwal, Manoj

  • Authors: Rezaeineshat, Afsaneh , Monjezi, Masoud , Mehrdanesh, Amirhossein , Khandelwal, Manoj
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 6, no. 2 (2020), p.
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  • Description: Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG.
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