Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Abstract Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.

[1]  Ali P. Yunus,et al.  Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. , 2020, The Science of the total environment.

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

[3]  R. Saidur,et al.  Application of support vector machine models for forecasting solar and wind energy resources: A review , 2018, Journal of Cleaner Production.

[4]  Hoang Nguyen,et al.  A Novel Artificial Intelligence Approach to Predict Blast-Induced Ground Vibration in Open-Pit Mines Based on the Firefly Algorithm and Artificial Neural Network , 2019, Natural Resources Research.

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

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

[7]  Masoud Monjezi,et al.  Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system , 2016, Environmental Earth Sciences.

[8]  Mahdi Hasanipanah,et al.  Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm , 2019, Engineering with Computers.

[9]  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.

[10]  Xiuzhi Shi,et al.  Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines , 2012 .

[11]  Willy Bauwens,et al.  Sobol' sensitivity analysis of a complex environmental model , 2011, Environ. Model. Softw..

[12]  Ahmet Teke,et al.  The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey , 2016 .

[13]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[14]  Mahdi Hasanipanah,et al.  Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting , 2016, Neural Computing and Applications.

[15]  Samuel Lukas,et al.  Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network , 2012, 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation.

[16]  Fang Wu,et al.  Steel plates fault diagnosis on the basis of support vector machines , 2015, Neurocomputing.

[17]  Nhat-Duc Hoang,et al.  Hybrid artificial intelligence approach based on metaheuristic and machine learning for slope stability assessment: A multinational data analysis , 2016, Expert Syst. Appl..

[18]  Hui Liu,et al.  Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options , 2018, IEEE Access.

[19]  Jonghyuk Kim,et al.  Occupancy Mapping and Surface Reconstruction Using Local Gaussian Processes With Kinect Sensors , 2013, IEEE Transactions on Cybernetics.

[20]  Wei Gao,et al.  Developing GPR model for forecasting the rock fragmentation in surface mines , 2018, Engineering with Computers.

[21]  Mahdi Hasanipanah,et al.  Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting , 2019, Engineering with Computers.

[22]  Masoud Monjezi,et al.  Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm , 2018, Engineering with Computers.

[23]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .

[24]  Masoud Monjezi,et al.  Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm , 2016, Bulletin of Engineering Geology and the Environment.

[25]  Ashwarya Sheel Wali,et al.  Comparative Study of Advance Smart Strain Approximation Method Using Levenberg-Marquardt and Bayesian Regularization Backpropagation Algorithm , 2020 .

[26]  Chase E. Golden,et al.  Comparison between random forest and gradient boosting machine methods for predicting Listeria spp. prevalence in the environment of pastured poultry farms. , 2019, Food research international.

[27]  H. Mansouri,et al.  A rock engineering systems based model to predict rock fragmentation by blasting , 2013 .

[28]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[29]  Hao Zhang,et al.  A UAV Detection Algorithm Based on an Artificial Neural Network , 2018, IEEE Access.

[30]  Hoang Nguyen.  Support vector regression approach with different kernel functions for predicting blast-induced ground vibration: a case study in an open-pit coal mine of Vietnam , 2019, SN Applied Sciences.

[31]  Neil D. Lawrence,et al.  Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems , 2017, IEEE Transactions on Automatic Control.

[32]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[33]  W. Zhou,et al.  Modeling the fragmentation of rock grains using computed tomography and combined FDEM , 2017 .

[34]  Thomas Gerig,et al.  Gaussian Process Morphable Models , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Russell C. Eberhart,et al.  Neural network PC tools: a practical guide , 1990 .

[36]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[37]  Xiaoling Zhang,et al.  A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression , 2018 .

[38]  Guanghui Li,et al.  Prediction of Pillar Stability for Underground Mines Using the Stochastic Gradient Boosting Technique , 2018, IEEE Access.

[39]  H. Pourghasemi,et al.  An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan , 2015, Natural Hazards.

[40]  Carsten Drebenstedt,et al.  Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network , 2019, Natural Resources Research.

[41]  Abbas Aghajani Bazzazi,et al.  Comparison Between Neural Networks and Multiple Regression Analysis to Predict Rock Fragmentation in Open-Pit Mines , 2014, Rock Mechanics and Rock Engineering.

[42]  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..

[43]  Xuan-Nam Bui,et al.  Developing a novel artificial intelligence model to estimate the capital cost of mining projects using deep neural network-based ant colony optimization algorithm , 2020, Resources Policy.

[44]  Hoang Nguyen,et al.  Proposing a novel predictive technique using M5Rules-PSO model estimating cooling load in energy-efficient building system , 2019, Engineering with Computers.

[45]  Masoud Monjezi,et al.  Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks , 2010 .

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

[47]  C. Drebenstedt,et al.  Application of PCA, SVR, and ANFIS for modeling of rock fragmentation , 2015, Arabian Journal of Geosciences.

[48]  P. M. Siva Raja,et al.  Lesion Localization and Extreme Gradient Boosting Characterization with Brain Tumor MRI Images , 2020 .

[49]  X. Bui,et al.  A Novel Combination of Whale Optimization Algorithm and Support Vector Machine with Different Kernel Functions for Prediction of Blasting-Induced Fly-Rock in Quarry Mines , 2020, Natural Resources Research.

[50]  V. J. Majd,et al.  Application of fuzzy inference system for prediction of rock fragmentation induced by blasting , 2015, Arabian Journal of Geosciences.

[51]  Dongrui Wu,et al.  A Switch-Mode Firefly Algorithm for Global Optimization , 2018, IEEE Access.

[52]  Christopher K. I. Williams,et al.  Using the Equivalent Kernel to Understand Gaussian Process Regression , 2004, NIPS.

[53]  J. Sanchidrián,et al.  Energy components in rock blasting , 2007 .

[54]  Ali Naseri,et al.  Reservoir oil viscosity determination using a rigorous approach , 2014 .

[55]  Hoang Nguyen,et al.  Optimizing Levenberg–Marquardt backpropagation technique in predicting factor of safety of slopes after two-dimensional OptumG2 analysis , 2019, Engineering with Computers.

[56]  Xuan-Nam Bui,et al.  Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique , 2019, Natural Resources Research.

[57]  M. Pravin Kumar,et al.  BACKPROPAGATION LEARNING ALGORITHM BASED ON LEVENBERG MARQUARDT ALGORITHM , 2012 .

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

[59]  Zhenpo Wang,et al.  State-of-Health Estimation for Lithium-Ion Batteries Based on the Multi-Island Genetic Algorithm and the Gaussian Process Regression , 2017, IEEE Access.

[60]  Hani S. Mitri,et al.  Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction , 2015, Natural Hazards.

[61]  Raymond R. Tan,et al.  An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation , 2020 .

[62]  M. P. Roy,et al.  Rock fragmentation control in opencast blasting , 2016 .

[63]  Yosoon Choi,et al.  Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm , 2020, Scientific Reports.

[64]  Jian Zhou,et al.  Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories , 2019, Safety Science.

[65]  Hong Anh Le,et al.  Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization , 2020 .

[66]  Miguel Pinzolas,et al.  Neighborhood based Levenberg-Marquardt algorithm for neural network training , 2002, IEEE Trans. Neural Networks.

[67]  M. Ataei,et al.  A new framework for evaluation of rock fragmentation in open pit mines , 2019, Journal of Rock Mechanics and Geotechnical Engineering.

[68]  Karbhari V. Kale,et al.  Comparison of Neural Network Training Functions for Prediction of Outgoing Longwave Radiation over the Bay of Bengal , 2020 .

[69]  Loke Kok Foong,et al.  Optimizing ANN models with PSO for predicting short building seismic response , 2019, Engineering with Computers.

[70]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[71]  J. Friedman.  Stochastic gradient boosting , 2002 .

[72]  Hoang Nguyen,et al.  A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam , 2018, Neural Computing and Applications.

[73]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[74]  Ali P. Yunus,et al.  Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.

[75]  N. Arunkumar,et al.  A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear , 2018, Future Gener. Comput. Syst..

[76]  M. Rahmanpour,et al.  Integration of sustainable development concepts in open pit mine design , 2015 .

[77]  Absalom E. Ezugwu,et al.  An Improved Firefly Algorithm for the Unrelated Parallel Machines Scheduling Problem With Sequence-Dependent Setup Times , 2018, IEEE Access.

[78]  Khosro Sayevand,et al.  A fresh view on particle swarm optimization to develop a precise model for predicting rock fragmentation , 2019, Engineering Computations.

[79]  Qingfu Zhang,et al.  Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model , 2010, IEEE Transactions on Evolutionary Computation.

[80]  Hoang Nguyen,et al.  Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Vibration Sensors and Support Vector Regression-Based Optimization Algorithms , 2019, Sensors.

[81]  Paulo Filipe T Lopes,et al.  Assessing and controlling of bench blasting-induced vibrations to minimize impacts to a neighboring community , 2018, Journal of Cleaner Production.

[82]  Jinu Gowthami Thankachi Raghuvaran,et al.  Prediction of specific wear rate for LM25/ZrO2 composites using Levenberg–Marquardt backpropagation algorithm , 2020 .

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

[84]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[85]  Ali P. Yunus,et al.  Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques , 2019, Scientific Reports.

[86]  Javier Bajo,et al.  Deep neural network architectures for social services diagnosis in smart cities , 2019, Future Gener. Comput. Syst..