Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach

An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters.Abstractتنفيذ عملية التفجير يؤثر بشكل كبير من الناحية المثالية تكاليف التعدين عموما. ويمكن تحقيق هذا الهدف عن طريق التنبؤ السليم وتخفيف flyrock وbackbreak. وحثت ضعف الأداء من نماذج تجريبية لتطبيق النهج الجديد. في هذه الورقة ، وقد بذلت محاولة لوضع نموذج جديد الاعصاب الوراثية للتنبؤ flyrock وbackbreak في منجم للنحاس Sungun وإيران. الاعتراف النموذج الأمثل مع هذا الأسلوب بالمقارنة مع الشبكة العصبية الكلاسيكي هو أسرع ومريحة. واستخدم الخوارزمية الجينية لتحسين المعلمات الشبكة العصبية. واعتبرت معلمات مثل عدد الخلايا العصبية في طبقة خفية ، معدل التعلم والزخم في بناء النموذج. تم فحص أداء نموذج من الأسلوب الإحصائي الذي ثبت كفاءة أعلى على الاطلاق من وضع نماذج الاعصاب الوراثية. حساسية التحليل أظهرت أن المعلمات الأكثر تأثيرا على flyrock هي النابعة ومسحوق للعامل في حين backbreak الناشئة والمسؤول عن تأخير في والمعلمات الأكثر فعالية.

[1]  D. K. Ingram,et al.  A Summary of Fatal Accidents Due to Flyrock and Lack of Blast Area Security in Surface Mining, 1989 to 1999 , 1900 .

[2]  U. Langefors,et al.  The modern technique of rock blasting. , 1968 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[7]  L. Darrell Whitley,et al.  Optimizing Neural Networks Using FasterMore Accurate Genetic Search , 1989, ICGA.

[8]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[9]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[10]  Hiroaki Kitano,et al.  Empirical Studies on the Speed of Convergence of Neural Network Training Using Genetic Algorithms , 1990, AAAI.

[11]  C. J. Konya,et al.  Rock blasting and overbreak control , 1991 .

[12]  Stefan Bornholdt,et al.  General asymmetric neural networks and structure design by genetic algorithms: a learning rule for temporal patterns , 1992, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[13]  Panos A. Ligomenides,et al.  GANNET: a genetic algorithm for searching topology and weight spaces in neural network design. The first step in finding a neural network solution , 1993 .

[14]  Wolfram Schiffmann,et al.  Application of Genetic Algorithms to the Construction of Topologies for Multilayer Perceptrons , 1993 .

[15]  K. Sarma.  Models for assessing the blasting performance of explosives , 1994 .

[16]  Tarek Hegazy,et al.  Developing Practical Neural Network Applications Using Back‐Propagation , 1994 .

[17]  P. N. Calder,et al.  Flyrock prediction and control in surface mine blasting , 1994 .

[18]  Larry R. Medsker,et al.  Genetic Algorithms and Neural Networks , 1995 .

[19]  Lakhmi C. Jain,et al.  Automatic generation of a neural network architecture using evolutionary computation , 1995, Proceedings Electronic Technology Directions to the Year 2000.

[20]  Lakhmi C. Jain,et al.  Neural Network Training Using Genetic Algorithms , 1996 .

[21]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[22]  James Noyes,et al.  Neural Network Training , 1996 .

[23]  Sushil Bhandari,et al.  Engineering rock blasting operations , 1997 .

[24]  A. Mohanty,et al.  Measurement of blast damage , 1997 .

[25]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[26]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[27]  M. Alvarez Grima,et al.  Forecasting rock trencher performance using fuzzy logic , 1999 .

[28]  M. Grima,et al.  Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness , 1999 .

[29]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

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

[31]  Imad A. Basheer,et al.  Selection of Methodology for Neural Network Modeling of Constitutive Hystereses Behavior of Soils , 2000 .

[32]  Yike Guo,et al.  A rule based fuzzy model for the prediction of petrophysical rock parameters , 2001 .

[33]  K. Koike,et al.  Characterizing Content Distributions of Impurities in a Limestone Mine Using a Feedforward Neural Network , 2003 .

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

[35]  Candan Gokceoglu,et al.  A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock , 2004, Eng. Appl. Artif. Intell..

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

[37]  T. N. Singh,et al.  An intelligent approach to prediction and control ground vibration in mines , 2005 .

[38]  B. S. Tezekici,et al.  Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks , 2006 .

[39]  K. M. Neaupane,et al.  Prediction of tunneling-induced ground movement with the multi-layer perceptron , 2006 .

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

[41]  R. Iqdour,et al.  A rule based fuzzy model for the prediction of solar radiation , 2006 .

[42]  A. S. Tawadrous,et al.  Prediction of surface crown pillar stability using artificial neural networks , 2007 .

[43]  T. Singh,et al.  Evaluation of blast-induced ground vibration predictors , 2007 .

[44]  Diego Andina,et al.  Application of Neural Networks , 2007 .

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

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

[47]  M. Kianpour,et al.  A FUZZY MODEL TO PREDICT THE UNIAXIAL COMPRESSIVE STRENGTH AND THE MODULUS OF ELASTICITY OF SHEMSHAK FORMATION SHALES , 2012 .