Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy.

[1]  M. N. Shanmukha Swamy,et al.  Neural methods for antenna array signal processing: a review , 2002, Signal Process..

[2]  A. K. Raina,et al.  Flyrock in bench blasting: a comprehensive review , 2014, Bulletin of Engineering Geology and the Environment.

[3]  Gérard Dreyfus,et al.  Neural networks - methodology and applications , 2005 .

[4]  Ercan Arpaz,et al.  Investigation of blast-induced ground vibrations in the Tülü boron open pit mine , 2013, Bulletin of Engineering Geology and the Environment.

[5]  Machinability of glass by abrasive waterjet , 2008 .

[6]  Umit Atici,et al.  Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network , 2011, Expert Syst. Appl..

[7]  Manoj Khandelwal,et al.  Artificial Neural Network as a Tool for Backbreak Prediction , 2014, Geotechnical and Geological Engineering.

[8]  Masoud Monjezi,et al.  Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic , 2009 .

[9]  Behnam Yazdani Bejarbaneh,et al.  Indirect measure of shale shear strength parameters by means of rock index tests through an optimized artificial neural network , 2014 .

[10]  Morteza Osanloo,et al.  Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting , 2012, Engineering with Computers.

[11]  R. J. Kuo,et al.  Integration of artificial neural network and MADA methods for green supplier selection , 2010 .

[12]  Masoud Monjezi,et al.  A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak , 2013 .

[13]  M. Monjezi,et al.  Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks , 2010 .

[14]  S. S. Kanchibotla,et al.  Modelling the impact of rockmass and blast design variation on blast fragmentation , 2002 .

[15]  Adem Kalinli,et al.  New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization , 2011 .

[16]  Aminaton Marto,et al.  Simulation of blasting-induced air overpressure by means of artificial neural networks , 2012 .

[17]  Aminaton Marto,et al.  Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm , 2015, Bulletin of Engineering Geology and the Environment.

[18]  Aminaton Marto,et al.  Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization , 2014 .

[19]  Edy Tonnizam Mohamad,et al.  Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach , 2015, Bulletin of Engineering Geology and the Environment.

[20]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[21]  Danial Jahed Armaghani,et al.  An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite , 2015, Bulletin of Engineering Geology and the Environment.

[22]  Masoud Monjezi,et al.  Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network , 2012, Neural Computing and Applications.

[23]  Patrick K. Simpson,et al.  Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations , 1990 .

[24]  Aminaton Marto,et al.  Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization , 2014, Arabian Journal of Geosciences.

[25]  Farhang Sereshki,et al.  A new methodology to predict backbreak in blasting operation , 2013 .

[26]  Danial Jahed.  An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young's modulus: a study on Main Range granite , 2014 .

[27]  M. Monjezi,et al.  Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method , 2013, Rock Mechanics and Rock Engineering.

[28]  FranciscoJavier AyalaCarcedo,et al.  Drilling and Blasting of Rocks , 2017 .

[29]  Heping Xie,et al.  Numerical investigation of blasting-induced crack initiation and propagation in rocks , 2007 .

[30]  N. Djordjevic,et al.  Influence of explosive energy on the strength of the rock fragments and SAG mill throughput , 2005 .

[31]  Masoud Monjezi,et al.  Prediction of rock fragmentation due to blasting using artificial neural network , 2011, Engineering with Computers.

[32]  Holger R. Maier,et al.  PREDICTING SETTLEMENT OF SHALLOW FOUNDATIONS USING NEURAL NETWORKS , 2002 .

[33]  Masoud Monjezi,et al.  Prediction of backbreak in open-pit blasting using fuzzy set theory , 2010, Expert Syst. Appl..

[34]  Ozgur Kisi,et al.  Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm , 2012 .

[35]  Danial Jahed Armaghani,et al.  Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks , 2015 .

[36]  M. Monjezi,et al.  Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach , 2012, Arabian Journal of Geosciences.