A Review of Artificial Intelligence Applications in Mining and Geological Engineering

Artificial intelligence (AI) is well-known as a robust technique that can support and improve the quality of human life. In the mining industry, applications of AI changed the sciences and technologies, as well as the performance of the mining industry, especially in mining and geological engineering. Smart mines were introduced and widely applied around the world with advanced technologies based on the applications of AI. This paper aims to provide a comprehensive view of AI applications in mining and geological engineering, as well as the ideas for studies in the future. The paper focuses on the published papers of AI applications in rock mechanics, mining method selection, mining equipment, drilling-blasting, slope stability, environmental issues, and relevant geological engineering. The advantages and disadvantages of AI applications in mining and geological engineering will be analyzed and discussed in detail.

[1]  Hoang Nguyen,et al.  Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping , 2019, Geomatics, Natural Hazards and Risk.

[2]  C. Nistor,et al.  Prevention and monitoring environmental impact of open pit coal mining activities. , 2018 .

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

[4]  D. V. Griffiths,et al.  SLOPE STABILITY ANALYSIS BY FINITE ELEMENTS , 1999 .

[5]  Mika Sillanpää,et al.  The use of low-cost adsorbents for wastewater purification in mining industries , 2013, Environmental Science and Pollution Research.

[6]  C. Özgen Karacan,et al.  Forecasting gob gas venthole production performances using intelligent computing methods for optimum methane control in longwall coal mines , 2009 .

[7]  Jianfeng Wang,et al.  Numerical modeling of geomaterial fracture using a cohesive crack model in grain-based DEM , 2020 .

[8]  Lanru Jing,et al.  A review of techniques, advances and outstanding issues in numerical modelling for rock mechanics and rock engineering , 2003 .

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

[10]  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.

[11]  G. Stacey,et al.  New slope stability considerations for deep open pit mines , 2003 .

[12]  Hadi Fattahi,et al.  Applying improved artificial neural network models to evaluate drilling rate index , 2017 .

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

[14]  Masoud Monjezi,et al.  Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques , 2016, Engineering with Computers.

[15]  Ayhan Kesimal,et al.  The study of a fuzzy set theory for the selection of an optimum coal transportation system from pit to the power plant , 1999 .

[16]  D. V. Griffiths,et al.  Unsaturated slope stability analysis with steady infiltration or evaporation using elasto‐plastic finite elements , 2005 .

[17]  Long Zhang,et al.  Evaluating and optimizing the effectiveness of mining equipment; the case of Chibuluma South underground mine , 2020 .

[18]  Alok Porwal,et al.  Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping , 2003 .

[19]  Roohollah Shirani Faradonbeh,et al.  Development of a precise model for prediction of blast-induced flyrock using regression tree technique , 2016, Environmental Earth Sciences.

[20]  A. A. Amusan,et al.  Characteristics of Soils and Crops’ Uptake of Metals in Municipal Waste Dump Sites in Nigeria , 2005 .

[21]  Yongsheng Zhou.  Application of Artificial Neural Network in Slope Stability Analysis , 2019 .

[22]  J. Caers,et al.  A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping , 2020 .

[23]  S. Emamgholizadeh,et al.  Estimating the amount of cadmium and lead in the polluted soil using artificial intelligence models , 2019, European Journal of Environmental and Civil Engineering.

[24]  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.

[25]  Bin Xu,et al.  Slope stability evaluation using Gaussian processes with various covariance functions , 2017, Appl. Soft Comput..

[26]  Amrita Das,et al.  Influence of magnetic effect, anisotropy, irregularity, initial stress and heterogeneity on propagation of SH-wave in an irregular pre-stressed magnetoelastic monoclinic sandwiched layer , 2017, Arabian Journal of Geosciences.

[27]  Victor Amoako Temeng,et al.  Novel approach to predicting blast-induced ground vibration using Gaussian process regression , 2019, Engineering with Computers.

[28]  X. Bui,et al.  Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study , 2019, Acta Geophysica.

[29]  M. Jung.  Heavy Metal Concentrations in Soils and Factors Affecting Metal Uptake by Plants in the Vicinity of a Korean Cu-W Mine , 2008, Sensors.

[30]  Masoud Monjezi,et al.  Predicting blast-induced ground vibration using various types of neural networks , 2010 .

[31]  A. L. Lund,et al.  砂浜海岸の生息場所でOlivella minuta(腹足類,マクラガイ)やHastula cimerea(腹足類,タケノコガイ)をTBTの指標に利用した監視 , 2015 .

[32]  Christian G. Daughton,et al.  Monitoring wastewater for assessing community health: Sewage Chemical-Information Mining (SCIM) , 2017, The Science of the total environment.

[33]  Seiichi Serikawa,et al.  Dust removal from high turbid underwater images using convolutional neural networks , 2017, Optics & Laser Technology.

[34]  R. Gholami,et al.  Heavy metal pollution assessment using support vector machine in the Shur River, Sarcheshmeh copper mine, Iran , 2012, Environmental Earth Sciences.

[35]  David J Walker,et al.  Uptake of heavy metals and As by Brassica juncea grown in a contaminated soil in Aznalcóllar (Spain): the effect of soil amendments. , 2005, Environmental pollution.

[36]  Shilpi Agarwal,et al.  Adsorption of heavy metals on conventional and nanostructured materials for wastewater treatment purposes: A review. , 2018, Ecotoxicology and environmental safety.

[37]  Sarat Kumar Das,et al.  Prediction of Factor of Safety For Slope Stability Using Advanced Artificial Intelligence Techniques , 2020 .

[38]  Salaheldin Elkatatny,et al.  Predicting Rate of Penetration Using Artificial Intelligence Techniques , 2018 .

[39]  Shalini Srivastava,et al.  Artificial Neural Network (ANN) design for Hg–Se interactions and their effect on reduction of Hg uptake by radish plant , 2010 .

[40]  Zongwei Ma,et al.  A review of soil heavy metal pollution from mines in China: pollution and health risk assessment. , 2014, The Science of the total environment.

[41]  Yuliya B. Lind,et al.  Artificial Neural Networks in Drilling Troubles Prediction (Russian) , 2014 .

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

[43]  L. Guilherme,et al.  Biosolids and heavy metals in soils , 2003 .

[44]  Vincenzo Balzani,et al.  Energy for a Sustainable World: From the Oil Age to a Sun-Powered Future , 2011 .

[45]  J. L. Daniels,et al.  Environmental issues from coal mining and their solutions , 2010 .

[46]  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.

[47]  Junjie Li,et al.  An artificial bee colony algorithm for locating the critical slip surface in slope stability analysis , 2013 .

[48]  Hoang Nguyen,et al.  A novel artificial intelligence technique for analyzing slope stability using PSO-CA model , 2019, Engineering with Computers.

[49]  Joseph H. M. Tah,et al.  Genetic algorithms application and testing for equipment selection , 1999 .

[50]  P. Nurmi.  Green Mining – A Holistic Concept for Sustainable and Acceptable Mineral Production , 2017 .

[51]  Lou Caccetta,et al.  Equipment Selection for Surface Mining: A Review , 2014, Interfaces.

[52]  Pan Weiguo,et al.  Corrigendum to: “Thermal conductivity enhancement on phase change materials for thermal energy storage: A review” , 2020 .

[53]  R. Zornoza,et al.  Multivariate statistical and GIS-based approach to evaluate heavy metals behavior in mine sites for , 2011 .

[54]  Weizhang Liang,et al.  Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach , 2019, Neural Computing and Applications.

[55]  A. Baghbanan,et al.  Numerical probabilistic analysis for slope stability in fractured rock masses using DFN-DEM approach , 2017 .

[56]  Chris Aldrich,et al.  Removal of pollutants from acid mine wastewater using metallurgical by-product slags , 2004 .

[57]  Mahdi Hasanipanah,et al.  A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure , 2016, Engineering with Computers.

[58]  W. A. Hobba.  Effects of underground mining and mine collapse on the hydrology of selected basins in West Virginia , 1993 .

[59]  Lin-ming Dou,et al.  Prevention and forecasting of rock burst hazards in coal mines , 2009 .

[60]  C. Özgen Karacan,et al.  Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks , 2008 .

[61]  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.

[62]  Miguel Tato Diogo,et al.  Engineering complex systems applied to risk management in the mining industry , 2017 .

[63]  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.

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

[65]  Yosoon Choi,et al.  Mapping hazardous mining-induced sinkhole subsidence using unmanned aerial vehicle (drone) photogrammetry , 2017, Environmental Earth Sciences.

[66]  Ruixiong Qi,et al.  Simulating the sustainable effect of green mining construction policies on coal mining industry of China , 2019, Journal of Cleaner Production.

[67]  Ivo Galić,et al.  AN ANOTHER WAY FOR OPEN PIT MINE DESIGN OPTIMIZATION – FLOATING SLOPES METHOD , 2009 .

[68]  M Serrano Joao,et al.  牧草変化のモニタリング:光学的OptRxR作物センサとGrassmaster IIキャパシタンスプローブ , 2016 .

[69]  Alexandra M. Newman,et al.  A Review of Operations Research in Mine Planning , 2010, Interfaces.

[70]  Ali Ismet Kanli,et al.  EQS : a computer software using fuzzy logic for equipment selection in mining engineering , 2006 .

[71]  E. Solgi,et al.  Soil Contamination of Metals in the Three Industrial Estates, Arak, Iran , 2012, Bulletin of Environmental Contamination and Toxicology.

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

[73]  Masoud Monjezi,et al.  Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach , 2015, Environmental Earth Sciences.

[74]  Hoang Nguyen,et al.  Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach , 2020, Appl. Soft Comput..

[75]  P. Das,et al.  OPTIMIZATION OF REDUCTION OF COPPER USING Stenotrophomonas maltophilia PD2 BIOMASS AND ARTIFICIAL NEURAL NETWORK MODELING , 2015 .

[76]  Dou Shi-qing.  The Assessment of Ventilation System For Underground Mines Based on Neural Network , 2005 .

[77]  P. Lu,et al.  Artificial Neural Networks and Grey Systems for the Prediction of Slope Stability , 2003 .

[78]  R. Hadji,et al.  Slope stability analysis in open pit mines of Jebel Gustar career, NE Algeria – a multi-steps approach , 2016 .

[79]  Yongliang Chen,et al.  Mapping mineral prospectivity using an extreme learning machine regression , 2017 .

[80]  Xuan-Nam Bui,et al.  A Novel Hybrid Model for Predicting Blast-Induced Ground Vibration Based on k-Nearest Neighbors and Particle Swarm Optimization , 2019, Scientific Reports.

[81]  Mohammad Ataei,et al.  The application of fuzzy analytic hierarchy process (FAHP) approach to selection of optimum underground mining method for Jajarm Bauxite Mine, Iran , 2009, Expert Syst. Appl..

[82]  G. H. Blackwell.  Estimation of large open pit haulage truck requirements , 1999 .

[83]  W. P. Diamond.  Methane control for underground coal mines , 1994 .

[84]  Mohammad Ataei,et al.  Mining method selection by multiple criteria decision making tools , 2004 .

[85]  Reza Shakoor Shahabi,et al.  Cutoff grade optimization in open pit mines using genetic algorithm , 2017 .

[86]  Saeid R. Dindarloo,et al.  Prediction of blast-induced ground vibrations via genetic programming , 2015 .

[87]  H. Bakhshandeh Amnieh,et al.  Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach , 2012 .

[88]  Bahram Rezai,et al.  Prediction of Co(II) and Ni(II) ions removal from wastewater using artificial neural network and multiple regression models , 2017 .

[89]  Chunguang Li,et al.  Slope stability analysis based on elasto‐plastic finite element method , 2005 .

[90]  Candan Gokceoglu,et al.  Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation , 2006 .

[91]  Aditya Kumar Patra,et al.  Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model , 2016, Air Quality, Atmosphere & Health.

[92]  Hamid Reza Pourghasemi,et al.  Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR) , 2020, Journal of Hydrology.

[93]  K. Nagesha,et al.  Development of statistical models to predict emission rate and concentration of particulate matters (PM) for drilling operation in opencast mines , 2019, Air Quality, Atmosphere & Health.

[94]  José M. Azcue,et al.  Environmental Impacts of Mining Activities: Emphasis on Mitigation and Remedial Measures , 2011 .

[95]  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.

[96]  Md. Tanweer Ahmad,et al.  Dynamic supplier selection approach for mining equipment company , 2019, Journal of Modelling in Management.

[97]  Li Ran.  Comprehensive depress & removal of dust caused by mining activities in underground coal mine , 2010 .

[98]  Seyed Amir Naghibi,et al.  Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping , 2017, Water Resources Management.

[99]  R. Roohi,et al.  Application of artificial neural network model for the identification the effect of municipal waste compost and biochar on phytoremediation of contaminated soils , 2020 .

[100]  Bashir Shokouh Saljoughi,et al.  A comparative analysis of artificial neural network (ANN), wavelet neural network (WNN), and support vector machine (SVM) data-driven models to mineral potential mapping for copper mineralizations in the Shahr-e-Babak region, Kerman, Iran , 2018, Applied Geomatics.

[101]  Xuan-Nam Bui,et al.  Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost , 2019, Natural Resources Research.

[102]  Panagiotis G. Asteris,et al.  A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model , 2020, Engineering with Computers.

[103]  Tianyou Chai,et al.  Source mapping and determining of soil contamination by heavy metals using statistical analysis, artificial neural network, and adaptive genetic algorithm , 2015 .

[104]  Ting-kai Nian,et al.  Three-dimensional strength-reduction finite element analysis of slopes: geometric effects , 2012 .

[105]  Erkan Topal,et al.  A review of soft computing technology applications in several mining problems , 2014, Appl. Soft Comput..

[106]  Shaofei Wu,et al.  Thermal conductivity enhancement on phase change materials for thermal energy storage: A review , 2020 .

[107]  Weiwen Dai,et al.  Mapping and functional analysis of heterochromatin protein 1 phosphorylation in the malaria parasite Plasmodium falciparum , 2019, Scientific Reports.

[108]  R. Dimitrakopoulos,et al.  A maximum upside / minimum downside approach to the traditional optimization of open pit mine design , 2007 .

[109]  Abbas Aghajani Bazzazi,et al.  A New Fuzzy Multi Criteria Decision Making Model for Open PIT Mines Equipment Selection , 2011, Asia Pac. J. Oper. Res..

[110]  Rafael Jimenez,et al.  A new open-pit mine slope instability index defined using the improved rock engineering systems approach , 2013 .

[111]  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.

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

[113]  Nick Vagenas,et al.  Genetic algorithms for reliability assessment of mining equipment , 2001 .

[114]  N. Lilic,et al.  Dust and Noise Environmental Impact Assessment and Control in Serbian Mining Practice , 2018 .

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

[116]  João Felipe Coimbra Leite Costa,et al.  Automated lithological classification using UAV and machine learning on an open cast mine , 2019 .

[117]  Celal Karpuz,et al.  Estimating Drilling Parameters for Diamond Bit Drilling Operations Using Artificial Neural Networks , 2008 .

[118]  Sadi Evren Seker,et al.  Estimation of Elastic Modulus of Intact Rocks by Artificial Neural Network , 2012, Rock Mechanics and Rock Engineering.

[119]  J. M. Duncan,et al.  Soil Strength and Slope Stability , 2005 .

[120]  Q. Sheng,et al.  Computation of the safety factor for slope stability using discontinuous deformation analysis and the vector sum method , 2017 .

[121]  Aurèle Parriaux,et al.  Geological uncertainties associated with 3-D subsurface models , 2006, Comput. Geosci..

[122]  Morteza Osanloo,et al.  A hybrid semi-quantitative approach for impact assessment of mining activities on sustainable development indexes , 2019, Journal of Cleaner Production.

[123]  K. Zhou,et al.  Prediction of rock burst classification using cloud model with entropy weight , 2016 .

[124]  Hesam Dehghani,et al.  A new model for mining method selection based on grey and TODIM methods , 2017 .

[125]  Yuliya B. Lind,et al.  Artificial Neural Networks in Drilling Troubles Prediction , 2014 .

[126]  Roohollah Shirani Faradonbeh,et al.  Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques , 2018, Engineering with Computers.

[127]  J. C. Fernández-Caliani,et al.  Heavy Metal Pollution in Soils Around the Abandoned Mine Sites of the Iberian Pyrite Belt (Southwest Spain) , 2009 .

[128]  Amin Barari,et al.  Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran) , 2009 .

[129]  Z. Asif,et al.  An integrated life cycle inventory and artificial neural network model for mining air pollution management , 2018, International Journal of Environmental Science and Technology.

[130]  Saurav Rukhaiyar,et al.  A polyaxial strength model for intact sandstone based on Artificial Neural Network , 2017 .

[131]  M. Ataei,et al.  A new approach to mining method selection based on modifying the Nicholas technique , 2010, Appl. Soft Comput..

[132]  付华,et al.  An optimized control of ventilation in coal mines based on artificial neural network , 2002 .

[133]  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.

[134]  N. V. Nghia,et al.  Building DEM for deep open-pit coal mines using DJI Inspire 2 , 2020 .

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

[136]  L. Krometis,et al.  Putting Corporate Social Responsibility to Work in Mining Communities: Exploring Community Needs for Central Appalachian Wastewater Treatment , 2015 .

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

[138]  K. Yetilmezsoy,et al.  Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells. , 2008, Journal of hazardous materials.

[139]  Weiwei Li,et al.  Modelling of dust removal in rotating packed bed using artificial neural networks (ANN) , 2017 .

[140]  Manoj Khandelwal,et al.  An intelligent approach to evaluate drilling performance , 2010, Neural Computing and Applications.

[141]  Luis Felipe Gonzalez,et al.  Towards the Development of a Low Cost Airborne Sensing System to Monitor Dust Particles after Blasting at Open-Pit Mine Sites , 2015, Sensors.

[142]  Francisco Herrera,et al.  Underground Mining Method Selection With the Hesitant Fuzzy Linguistic Gained and Lost Dominance Score Method , 2018, IEEE Access.

[143]  J.J.L. Du Plessis,et al.  Control strategies for coal dust and methane explosions in underground coal mines : current South African research and development initiatives , 1999 .

[144]  B. J. Alloway,et al.  Heavy Metals in Soils: Trace Metals and Metalloids in Soils and their Bioavailability , 2013 .

[145]  Alex Spathis,et al.  Mining and Rock Construction Technology Desk Reference : Rock Mechanics, Drilling & Blasting , 2010 .

[146]  Hoang Nguyen,et al.  Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO , 2019, Applied Sciences.

[147]  Hossein Moayedi,et al.  The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes , 2019, ISPRS Int. J. Geo Inf..

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

[149]  Danial Jahed Armaghani,et al.  Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system , 2020, Bulletin of Engineering Geology and the Environment.

[150]  Baiyu Zhang,et al.  Removal of Heavy Metals from Mining Wastewater by Micellar-Enhanced Ultrafiltration (MEUF): Experimental Investigation and Monte Carlo-Based Artificial Neural Network Modeling , 2017, Water, Air, & Soil Pollution.

[151]  Anne Probst,et al.  Metal contamination of soils and crops affected by the Chenzhou lead/zinc mine spill (Hunan, China). , 2005, The Science of the total environment.

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

[153]  Charles A Kliche.  Rock Slope Stability , 1999 .

[154]  Seyed Amir Naghibi,et al.  GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran , 2015, Environmental Monitoring and Assessment.

[155]  C. Özgen Karacan,et al.  Coal mine methane: A review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction , 2011 .

[156]  Jian-Hua Yin,et al.  A three-dimensional slope stability analysis method using the upper bound theorem: Part I: theory and methods , 2001 .

[157]  E. Bozorgebrahimi,et al.  Sizing equipment for open pit mining – a review of critical parameters , 2003 .

[158]  Mark J. Nieuwenhuijsen,et al.  Human exposure modelling for chemical risk assessment: a review of current approaches and research and policy implications , 2006 .

[159]  Mahdi Hasanipanah,et al.  Developing a new hybrid-AI model to predict blast-induced backbreak , 2017, Engineering with Computers.

[160]  Amina Lamghari,et al.  Hyper-heuristic approaches for strategic mine planning under uncertainty , 2018, Comput. Oper. Res..

[161]  Hossein Bonakdari,et al.  Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting , 2018, Engineering with Computers.

[162]  Morteza Osanloo,et al.  Optimal Open Pit Mining Equipment Selection Using Fuzzy Multiple Attribute Decision Making Approach , 2009 .

[163]  Nguyen Thoi Trung,et al.  Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of eco-protection slopes , 2020 .

[164]  M. Sulaiman,et al.  Assessment of Concentrations and Ecological Risk of Heavy Metals at Resident and Remediated Soils of Uncontrolled Mining Site at Dareta Village, Zamfara, Nigeria , 2019, Journal of Applied Sciences and Environmental Management.

[165]  Li Xibing,et al.  Prediction of rockburst classification using Random Forest , 2013 .

[166]  Leslie George Tham,et al.  Numerical modelling of soil nails in loose fill slope under surcharge loading , 2009 .

[167]  Luis Felipe Gonzalez,et al.  A Methodology to Monitor Airborne PM10 Dust Particles Using a Small Unmanned Aerial Vehicle , 2017, Sensors.

[168]  Arunav Chakraborty,et al.  Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN) , 2017, Arabian Journal of Geosciences.

[169]  Nadhir Al-Ansari,et al.  Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models , 2020, Water.

[170]  Ali Kahriman,et al.  Environmental impacts of bench blasting at Hisarcik Boron open pit mine in Turkey , 2006 .

[171]  Volkan Atalay,et al.  Ore-age: a hybrid system for assisting and teaching mining method selection , 2003, Expert Syst. Appl..

[172]  Le Van Canh,et al.  Volume computation of quarries in Vietnam based on Unmanned Aerial Vehicle (UAV) data , 2020 .

[173]  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.

[174]  Y. M. Cheng,et al.  Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods , 2007 .

[175]  Boris Jeremić,et al.  Finite Element Methods for 3D Slope Stability Analysis , 2000 .

[176]  Masoud Monjezi,et al.  Prediction of blast-induced ground vibration using artificial neural networks , 2011 .

[177]  Abbas Majdi,et al.  Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses , 2010 .

[178]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[179]  C. Özgen Karacan,et al.  Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams , 2007 .

[180]  A. Facchinelli,et al.  Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. , 2001, Environmental pollution.

[181]  Damodar Maity,et al.  Multiverse Optimisation Algorithm for Capturing the Critical Slip Surface in Slope Stability Analysis , 2019, Geotechnical and Geological Engineering.

[182]  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.

[183]  Hongwei Liu,et al.  A nonlinear goal-programming-based DE and ANN approach to grade optimization in iron mining , 2015, Neural Computing and Applications.

[184]  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.

[185]  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..

[186]  Masoud Monjezi,et al.  An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine,Iran , 2014 .

[187]  Norbert Enzinger,et al.  Joining of Aluminum and Steel in Car Body Manufacturing , 2011 .

[188]  Mahdi Hasanipanah,et al.  An intelligent based-model role to simulate the factor of safe slope by support vector regression , 2018, Engineering with Computers.

[189]  M. K. Özfırat,et al.  A fuzzy method for selecting underground coal mining method considering mechanization criteria , 2012, Journal of Mining Science.

[190]  Guowei Ma,et al.  Footwall slope stability analysis with the numerical manifold method , 2011 .

[191]  Oliver Schauerte,et al.  Titanium in Automotive Production , 2003 .

[192]  Michael Sakellariou,et al.  A study of slope stability prediction using neural networks , 2005 .

[193]  Sam Hampton,et al.  Geologic and geomorphic controls on rockfall hazard: how well do past rockfalls predict future distributions? , 2019, Natural Hazards and Earth System Sciences.

[194]  Peter R. Stevens,et al.  A review of the effects of earthquakes on underground mines , 1977 .

[195]  Emmanuel John M. Carranza,et al.  Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province, Western India , 2003 .

[196]  Xuxu Yang,et al.  Impacts of bench blasting vibration on the stability of the surrounding rock masses of roadways , 2018 .

[197]  Masoud Monjezi,et al.  Feasibility of indirect determination of blast induced ground vibration based on support vector machine , 2015 .

[198]  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.

[199]  David A. Wood,et al.  A machine learning approach to predict drilling rate using petrophysical and mud logging data , 2019, Earth Science Informatics.

[200]  Rennie B. Kaunda,et al.  New artificial neural networks for true triaxial stress state analysis and demonstration of intermediate principal stress effects on intact rock strength , 2014 .

[201]  Martha Benavente,et al.  Sorption of heavy metals from gold mining wastewater using chitosan , 2011 .

[202]  Masoud Monjezi,et al.  Forecasting blast-induced ground vibration developing a CART model , 2017, Engineering with Computers.

[203]  Yosoon Choi,et al.  Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System , 2019, Natural Resources Research.

[204]  Aminaton Marto,et al.  Neuro-fuzzy technique to predict air-overpressure induced by blasting , 2015, Arabian Journal of Geosciences.

[205]  Abbas Heydari,et al.  Evaluation of the parameters affecting the Schmidt rebound hammer reading using ANFIS method , 2018 .

[206]  Xiaolin Tang,et al.  Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study , 2018, Comput. Ind. Eng..

[207]  Arun S. Mujumdar,et al.  CFD simulation of methane dispersion and innovative methane management in underground mining faces , 2014 .

[208]  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..

[209]  Yosoon Choi,et al.  Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry , 2016 .

[210]  Bhalchandra V. Gokhale,et al.  Rotary Drilling and Blasting in Large Surface Mines , 2010 .

[211]  Aldenor G. Santos,et al.  Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles , 2019, Scientific Reports.

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