Machine learning based prediction of flyrock distance in rock blasting:A safe and sustainable mining approach  

作　　者：Blessing Olamide Taiwo Yewuhalashet Fissha Shahab Hosseini Mohammad Khishe Esma Kahraman Babatunde Adebayo Mohammed Sazid Patrick Adeniyi Adesida Oluwaseun Victor Famobuwa Joshua Oluwaseyi Faluyi Adams Abiodun Akinlabi 

机构地区：[1]Department of Mining Engineering,Federal University of Technology Akure,Akure 340001,Nigeria [2]HNF Global Resources Limited,Akoko Edo,Igarra 312101,Nigeria [3]Department of Geosciences,Geotechnology and Materials Engineering for Resources,Graduate School of International Resource Sciences,Akita University,Akita 010-8502,Japan [4]Department of Mining Engineering,Aksum University,Aksum 7080,Tigray,Ethiopia [5]Faculty of Engineering,Tarbiat Modares University,Tehran 14115-111,Iran [6]Department of Electrical Engineering,Imam Khomeini Naval Science University of Nowshahr,Nowshahr 46517,Iran [7]Applied Science Research Center,Applied Science Private University,Amman 11937,Jordan [8]Department of Mining Engineering,Cukurova University,Adana 01250,Turkey [9]Department of Mining Engineering,King Abdulaziz University,Jeddah 21589,Saudi Arabia [10]Department of Mining,West Virginia University,Morgantown 26506,USA

出　　处：《Green and Smart Mining Engineering》2024年第3期346-361,共16页绿色与智能矿业工程（英文）

摘　　要：Flyrock is a significant environmental and safety concern in mining and construction.It arises from various geological and blast design factors,posing risks to workers,machinery,and nearby structures.This study examined how these factors affect the rate and distance of flyrock projections caused by blasts.To address this issue,advanced machine learning(ML)models were used to predict flyrock distances in the Akoko Edo dolomite quarries.The models examined included bidirectional recurrent neural networks(BRNNs),support vector regression(SVR)with different kernels(SVR-S,SVR-RBF,SVR-L,SVR-P),long short-term memory(LSTM)networks,and random forest(RF)algorithms.A case study was conducted using 258 blasting data samples to develop these models.Key factors influencing flyrock were identified:blast hole burden distance,maximum instantaneous charge,and rock brittleness index.Using these factors,a flyrock possibility assessment chart was created to enhance the safety of small-scale mining operations.The model’s prediction accuracy was evaluated using correlation coefficients and four performance metrics.The LSTM model stood out,achieving the highest coefficient of correlation(R2=0.99)for both training and testing datasets.This indicates that the LSTM model accurately predicts blast-induced flyrock distance.The study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants(SVR-S,SVR-L,and SVR-P).In conclusion,the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

关 键 词：BLASTING Flyrock Safety chart Mine production reliability Long short-term memory networks 

分 类 号：TD235[矿业工程—矿井建设] TP181[自动化与计算机技术—控制理论与控制工程]