Indirect evaluation of the influence of rock boulders in blasting to the geohazard:Unearthing geologic insights fused with tree seed based LSTM algorithm  

作　　者：Blessing Olamide Taiwo Shahab Hosseini Yewuhalashet Fissha Kursat Kilic Omosebi Akinwale Olusola NSri Chandrahas Enming Li Adams Abiodun Akinlabi Naseer Muhammad Khan 

机构地区：[1]Department of Mining Engineering,Federal University of Technology,Akure,340001,Nigeria [2]HNF Global Resources Limited,Akoko Edo,Nigeria [3]Faculty of Engineering,Tarbiat Modares University,Tehran,14117,Iran [4]Department of Geosciences,Geotechnology and Materials Engineering for Resources,Graduate School of International Resource Sciences,Akita University,Akita,0108502,Japan [5]Department of Mining Engineering,Aksum University,Aksum,7080,Ethiopia [6]Mine Planning Division,GMMCO Technology Services(GTS),Hyderabad,500008,India [7]School of Mine and Energy,Polytechnic University of Madrid,Madrid,28003,Spain [8]Department of Sustainable Advanced Geomechanical Engineering,Military College of Engineering,National University of Sciences and Technology,Risalpur,23200,Pakistan [9]MEU Research Unit,Middle East University,Amman,11831,Jordan

出　　处：《Geohazard Mechanics》2024年第4期244-257,共14页岩土灾变力学(英文)

基　　金：funded by China Scholarship Council (No.202006370006).

摘　　要：Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.

关 键 词：Oversize boulder BLASTING Image analysis Downstream operation Artificial intelligence 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]