Prediction of rock fragmentation due to blasting using artificial neural network

Prediction of rock fragmentation is essential for optimizing blasting operation. Fragmentation depends on many parameters such as rock mass properties, blast geometry and explosive properties. In this paper, artificial neural network (ANN) method is implemented to develop a model to predict rock fragmentation due to blasting in an iron ore mine. In the developing of the proposed model eight parameters such as hole diameter, burden, powder factor, blastability index, etc., were incorporated. Training of the model was performed by back-propagation algorithm using 220 datasets. A four-layer ANN was found to be optimum with architecture 10-9-7-1. Sensitivity analysis revealed that the most effective parameters on rock fragmentation are blastability index (G), charge per delay (J), burden (C), SMR (F) and powder factor (E).

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

[2]  Yong Lu,et al.  Underground blast induced ground shock and its modelling using artificial neural network , 2005 .

[3]  Yingjie Yang,et al.  A hierarchical analysis for rock engineering using artificial neural networks , 1997 .

[4]  Student,et al.  On Testing Varieties of Cereals , 1923 .

[5]  Masoud Monjezi,et al.  Prediction and controlling of flyrock in blasting operation using artificial neural network , 2011 .

[6]  S. Kahramana,et al.  Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks , 2005 .

[7]  George W. Snedecor,et al.  Statistical methods applied to experiments in agriculture and biology. , 1946 .

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

[9]  T. Singh,et al.  Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach , 2006 .

[10]  Branko Božić.  CONTROL OF FRAGMENTATION BY BLASTING , 1998 .

[11]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[12]  Alsmadi M.Kh.S.,et al.  Back Propagation Algorithm: The Best Algorithm Among the Multi-layer Perceptron Algorithm , 2009 .

[13]  S. S. Kanchibotla,et al.  Optimum Blasting? Is it Minimum Cost Per Broken Rock or Maximum Value Per Broken Rock? , 2003 .

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

[15]  Yves Chauvin,et al.  Backpropagation: theory, architectures, and applications , 1995 .

[16]  Steven D. Levitt,et al.  Field Experiments in Economics: the Past, the Present, and the Future , 2008 .

[17]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[18]  T. N. Singh,et al.  Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks , 2001 .

[19]  M. P. Roy,et al.  Application of artificial neural network in mining industry , 2004 .

[20]  K. Neaupane,et al.  Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya , 2004 .

[21]  S. Kahraman,et al.  Performance Prediction of Circular Diamond Saws from Mechanical Rock Properties in Cutting Carbonate Rocks , 2007 .

[22]  T. N. Singh,et al.  Prediction of Blast Induced Air Overpressure in Opencast Mine , 2005 .

[23]  W. A. Hustrulid,et al.  Blasting principles for open pit mining , 1999 .

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

[25]  Masoud Monjezi,et al.  Evaluation of effect of blasting pattern parameters on back break using neural networks , 2008 .

[26]  A. S. Tawadrous.  EVALUATION OF ARTIFICIAL NEURAL NETWORKS AS A RELIABLE TOOL IN BLAST DESIGN , 2006 .

[27]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[28]  R. Hecht-Nielsen,et al.  Neurocomputing: picking the human brain , 1988, IEEE Spectrum.

[29]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[30]  Indra Narayan Kar,et al.  Estimation of furnace exit gas temperature (FEGT) using optimized radial basis and back-propagation neural networks , 2008 .

[31]  E. Dodd,et al.  Statistical Methods—Applied to Experiments in Agriculture and Biology , 1939 .

[32]  Mario A. Morin,et al.  Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz-Ram model , 2006, Comput. Geosci..

[33]  Yong-Hun Jong,et al.  Influence of geological conditions on the powder factor for tunnel blasting , 2004 .