Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

作     者：Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal Enming Li;Fenghao Yang;Meiheng Ren;Xiliang Zhang;Jian Zhou;Manoj Khandelwal

作者机构：School of Resources and Safety EngineeringCentral South UniversityChangsha410083China Universidad Politecnica de Madrid-ETSI Minas y EnergiaRios Rosas 21Madrid28003Spain College of Locomotive and Rolling Stock EngineeringDalian Jiaotong UniversityDalian116028China Department of MathematicsUniversity of California Santa BarbaraSanta BarbaraCA93106USA State Key Laboratory of Safety and Health for Metal MinesMaanshan243000China School of EngineeringInformation Technology and Physical SciencesFederation University AustraliaBallaratVIC3350Australia 

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：1380-1397页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0802[工学-机械工程] 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：funded by the National Natural Science Foundation of China(Grant No.42177164) the Innovation-Driven Project of Central South University(Grant No.2020CX040) supported by China Scholarship Council(Grant No.202006370006) 

主　　题：Blasting mean fragment size e-support vector regression(e-SVR) V-support vector regression(v-SVR) Meta-heuristic algorithms Intelligent prediction 

摘      要：The main purpose of blasting operation is to produce desired and optimum mean size rock *** or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production ***,accurate prediction of rock fragmentation is crucial in blasting *** fragment size(MFS) is a crucial index that measures the goodness of blasting *** the past decades,various models have been proposed to evaluate and predict blasting *** these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential *** this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,*** search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the *** prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI *** all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting *** types of mathematical indices,*** square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction *** R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing ***,sensitivity analysis is performed to understand the influence of input parameters on *** shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.