Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

作     者：Bhatawdekar Ramesh Murlidhar Hoang Nguyen Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad Bhatawdekar Ramesh Murlidhar;Hoang Nguyen;Jamal Rostami;XuanNam Bui;Danial Jahed Armaghani;Prashanth Ragam;Edy Tonnizam Mohamad

作者机构：Centre of Tropical Geoengineering(Geotropik)School of Civil EngineeringFaculty of EngineeringUniversiti Teknologi MalaysiaSkudaiJohor81310Malaysia Department of Mining EngineeringIndian Institute of TechnologyKharagpur721302India Department of Surface MiningMining FacultyHanoi University of Mining and GeologyHanoi100000Viet Nam Innovations for Sustainable and Responsible Mining(ISRM)GroupHanoi University of Mining and GeologyHanoi100000Viet Nam Department of Mining EngineeringEarth Mechanics InstituteColorado School of MinesGoldenCO80401USA Department of Urban PlanningEngineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinsk454080Russia Department of ECEKakatiya Institute of Technology and ScienceWarangal506015India 

出 版 物：《Journal of Rock Mechanics and Geotechnical Engineering》 (岩石力学与岩土工程学报（英文版）)

年 卷 期：2021年第13卷第6期

页      面：1413-1427页

核心收录：

学科分类：12[管理学] 081901[工学-采矿工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0819[工学-矿业工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Center for Mining Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG) Hanoi Vietnam 

主　　题：Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA) 

摘      要：In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive ***,use of explosives may lead to the flyrock *** can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working ***,prediction of flyrock is of high *** this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried *** hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field *** collected data include blasting parameters and rock mass ***-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass ***-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were *** performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.