Use of Intelligent Methods to Design Effective Pattern Parameters of Mine Blasting to Minimize Flyrock Distance

Flyrock is one of the most important environmental issues in mine blasting, which can affect equipment, people and could cause fatal accidents. Therefore, minimization of this environmental issue of blasting must be considered as the ultimate objective of many rock removal projects. This paper describes a new minimization procedure of flyrock using intelligent approaches, i.e., artificial neural network (ANN) and particle swarm optimization (PSO) algorithms. The most effective factors of flyrock were used as model inputs while the output of the system was set as flyrock distance. In the initial stage, an ANN model was constructed and proposed with high degree of accuracy. Then, two different strategies according to ideal and engineering condition designs were considered and implemented using PSO algorithm. The two main parameters of PSO algorithm for optimal design were obtained as 50 for number of particle and 1000 for number of iteration. Flyrock values were reduced in ideal condition to 34 m; while in engineering condition, this value was reduced to 109 m. In addition, an appropriate blasting pattern was proposed. It can be concluded that using the proposed techniques and patterns, flyrock risks in the studied mine can be significantly minimized and controlled.

[1]  R. Hecht-Nielsen.  Kolmogorov''s Mapping Neural Network Existence Theorem , 1987 .

[2]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[3]  D. R. Hush,et al.  Classification with neural networks: a performance analysis , 1989, IEEE 1989 International Conference on Systems Engineering.

[4]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[5]  S Mangrulkar,et al.  Artificial neural systems. , 1990, ISA transactions.

[6]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[7]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[8]  Brian D. Ripley,et al.  Statistical aspects of neural networks , 1993 .

[9]  Changfeng Wang,et al.  A theory of generalization in learning machines with neural network applications , 1994 .

[10]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[11]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

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

[13]  I. Kanellopoulos,et al.  Strategies and best practice for neural network image classification , 1997 .

[14]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[15]  F. Waismann.  The Logical Calculus , 1997 .

[16]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

[18]  M. S. Voss,et al.  Social programming using functional swarm optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[19]  G. L. Mowrey,et al.  Blasting injuries in surface mining with emphasis on flyrock and blast area security. , 2004, Journal of Safety Research.

[20]  Kevin L. Priddy,et al.  Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68) , 2005 .

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

[22]  V. Kecojevic,et al.  Flyrock phenomena and area security in blasting-related accidents , 2005 .

[23]  Kuriakose Athappilly,et al.  A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models , 2005, Expert Syst. Appl..

[24]  Kevin L. Priddy,et al.  Artificial neural networks - an introduction , 2005, Tutorial text series.

[25]  Kevin L. Priddy,et al.  Artificial Neural Networks: An Introduction (SPIE Tutorial Texts in Optical Engineering, Vol. TT68) , 2005 .

[26]  Bulent Tiryaki,et al.  Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees , 2008 .

[27]  T. N. Singh,et al.  Prediction of blast-induced ground vibration using artificial neural network , 2009 .

[28]  T. N. Singh,et al.  Intelligent systems for ground vibration measurement: a comparative study , 2011, Engineering with Computers.

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

[30]  Masoud Monjezi,et al.  Development of a fuzzy model to predict flyrock in surface mining , 2011 .

[31]  Jian Zhou,et al.  Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction , 2012 .

[32]  M. Mohandes.  Modeling global solar radiation using Particle Swarm Optimization (PSO) , 2012 .

[33]  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.

[34]  M. Ahmadi,et al.  New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept , 2012 .

[35]  Amir Hossein Alavi,et al.  Krill herd: A new bio-inspired optimization algorithm , 2012 .

[36]  Mohammad Ataei,et al.  Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation , 2012, Arabian Journal of Geosciences.

[37]  Mohammad Ataei,et al.  Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines , 2012 .

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

[39]  Amir Hossein Gandomi,et al.  A multi-stage particle swarm for optimum design of truss structures , 2013, Neural Computing and Applications.

[40]  Xin-She Yang,et al.  1 – Metaheuristic Algorithms in Modeling and Optimization , 2013 .

[41]  Koohyar Faizi,et al.  A simulation approach to predict blasting-induced flyrock and size of thrown rocks , 2013 .

[42]  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.

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

[44]  Danial Jahed Armaghani,et al.  A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network , 2014, TheScientificWorldJournal.

[45]  T. N. Singh,et al.  Prediction of blast-induced flyrock in Indian limestone mines using neural networks , 2014 .

[46]  A. Gandomi.  Interior search algorithm (ISA): a novel approach for global optimization. , 2014, ISA transactions.

[47]  Danial Jahed Armaghani,et al.  Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods , 2015, Engineering with Computers.

[48]  Ratnesh Trivedi,et al.  Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS , 2015, Geotechnical and Geological Engineering.

[49]  Mahdi Hasanipanah,et al.  Several non-linear models in estimating air-overpressure resulting from mine blasting , 2015, Engineering with Computers.

[50]  Aminaton Marto,et al.  Prediction of blast-induced air overpressure: a hybrid AI-based predictive model , 2015, Environmental Monitoring and Assessment.

[51]  Danial Jahed Armaghani,et al.  Rock strength estimation: a PSO-based BP approach , 2016, Neural Computing and Applications.

[52]  Hani S. Mitri,et al.  Classification of Rockburst in Underground Projects: Comparison of Ten Supervised Learning Methods , 2016, J. Comput. Civ. Eng..

[53]  Haitao Liu,et al.  Anisotropies in Mechanical Behaviour, Thermal Expansion and P-Wave Velocity of Sandstone with Bedding Planes , 2016, Rock Mechanics and Rock Engineering.

[54]  Danial Jahed Armaghani,et al.  Prediction of the durability of limestone aggregates using computational techniques , 2016, Neural Computing and Applications.

[55]  Masoud Monjezi,et al.  Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique , 2016, Bulletin of Engineering Geology and the Environment.

[56]  Mahdi Hasanipanah,et al.  Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting , 2016, Rock Mechanics and Rock Engineering.

[57]  Danial Jahed Armaghani,et al.  Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm , 2018, Neural Computing and Applications.

[58]  Danial Jahed Armaghani,et al.  A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels , 2019, Bulletin of Engineering Geology and the Environment.

[59]  Danial Jahed Armaghani,et al.  Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition , 2017 .

[60]  M. Hajihassani,et al.  Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review , 2018, Geotechnical and Geological Engineering.

[61]  Danial Jahed Armaghani,et al.  Three hybrid intelligent models in estimating flyrock distance resulting from blasting , 2018, Engineering with Computers.

[62]  X. Bui,et al.  Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam , 2018, SN Applied Sciences.

[63]  Edy Tonnizam Mohamad,et al.  Overbreak prediction and optimization in tunnel using neural network and bee colony techniques , 2018, Engineering with Computers.

[64]  X. Bui,et al.  Predicting Blast-Induced Air Overpressure: A Robust Artificial Intelligence System Based on Artificial Neural Networks and Random Forest , 2018, Natural Resources Research.

[65]  Jian Zhou,et al.  Multi-planar detection optimization algorithm for the interval charging structure of large-diameter longhole blasting design based on rock fragmentation aspects , 2018 .

[66]  Jian Zhou,et al.  A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network , 2019, Engineering with Computers.

[67]  Mohammadreza Koopialipoor,et al.  A new approach for estimation of rock brittleness based on non-destructive tests , 2019, Nondestructive Testing and Evaluation.

[68]  Danial Jahed Armaghani,et al.  The use of new intelligent techniques in designing retaining walls , 2019, Engineering with Computers.

[69]  Danial Jahed Armaghani,et al.  Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions , 2019, Soft Comput..

[70]  Danial Jahed Armaghani,et al.  Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance , 2019, Engineering with Computers.

[71]  A. Marto,et al.  Application of several optimization techniques for estimating TBM advance rate in granitic rocks , 2019, Journal of Rock Mechanics and Geotechnical Engineering.

[72]  Aydin Azizi,et al.  A new methodology for optimization and prediction of rate of penetration during drilling operations , 2019, Engineering with Computers.

[73]  Manoj Khandelwal,et al.  Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques , 2019, Engineering with Computers.

[74]  Hoang Nguyen,et al.  Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques , 2019, Natural Resources Research.

[75]  Hoang Nguyen,et al.  A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms , 2019, Appl. Soft Comput..

[76]  Ebrahim Noroozi Ghaleini,et al.  Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN , 2019, Environmental Earth Sciences.

[77]  Jian Zhou,et al.  Optimal Charge Scheme Calculation for Multiring Blasting Using Modified Harries Mathematical Model , 2019, Journal of Performance of Constructed Facilities.

[78]  Ahmadreza Hedayat,et al.  Application of deep neural networks in predicting the penetration rate of tunnel boring machines , 2019, Bulletin of Engineering Geology and the Environment.