Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine  

作　　者：Mehdi Jamei Mahdi Hasanipanah Masoud Karbasi Iman Ahmadianfar Somaye Taherifar 

机构地区：[1]Faculty of Engineering,Shohadaye Hoveizeh Campus of Technology,Shahid Chamran University of Ahvaz,Dashte Azadegan,Iran [2]Department of Mining Engineering,University of Kashan,Kashan,Iran [3]Institute of Research and Development,Duy Tan University,Da Nang,550000,Vietnam [4]Department of Water Engineering,Faculty of Agriculture,University of Zanjan,Zanjan,Iran [5]Department of Civil Engineering,Behbahan Khatam Alanbia University of Technology,Behbahan,Iran [6]Department of Computer Sciences,Faculty of Mathematics and Computer Sciences,Shahid Chamran University of Ahvaz,Ahvaz,Iran

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2021年第6期1438-1451,共14页岩石力学与岩土工程学报（英文版）

摘　　要：Blasting is a common method of breaking rock in surface mines.Although the fragmentation with proper size is the main purpose,other undesirable effects such as flyrock are inevitable.This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm,called kernel extreme learning machine(KELM),by which the flyrock distance(FRD) is predicted.Furthermore,the other three data-driven models including local weighted linear regression(LWLR),response surface methodology(RSM) and boosted regression tree(BRT) are also developed to validate the main model.A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing,burden,stemming length and powder factor data as inputs and FRD as target.Afterwards,the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools.Finally,the results verify that the proposed KELM model on account of highest correlation coefficient(R) and lowest root mean square error(RMSE) is more computationally efficient,leading to better predictive capability compared to LWLR,RSM and BRT models for all data sets.

关 键 词：BLASTING Flyrock distance Kernel extreme learning machine(KELM) Local weighted linear regression(LWLR) Response surface methodology(RSM) 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TD235[自动化与计算机技术—控制科学与工程]