Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Chengyu Xie; Hoang Nguyen; Xuan-Nam Bui; Yosoon Choi; Jian Zhou; Thao Nguyen-Trang

首页> 中文期刊> 《地学前缘：英文版》 >Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

开具论文收录证明 >>

期刊封面封底目录下载 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.

著录项

来源
《地学前缘：英文版》 |2021年第3期|458-472|共15页
作者
Chengyu Xie; Hoang Nguyen; Xuan-Nam Bui; Yosoon Choi; Jian Zhou; Thao Nguyen-Trang;
展开▼
作者单位

School of Environment and Resources;

Xiangtan University;

Xiangtan 411105;

China;

Institute of Research and Development;

Duy Tan University;

Da Nang 550000;

Viet Nam;

Department of Surface Mining;

Mining Faculty;

Hanoi University of Mining and Geology;

18 Pho Vien;

Duc Thang ward;

Bac Tu Liem district;

Hanoi 100000;

Viet Nam;

Center for Mining;

Electro-Mechanical research;

Hanoi University of Mining and Geology;

18 Pho Vien;

Duc Thang ward;

Bac Tu Liem district;

Hanoi 100000;

Viet Nam;

Department of Energy Resources Engineering;

Pukyong National University;

Busan 48513;

Republic of Korea;

School of Resources and Safety Engineering;

Central South University;

Changsha 410083;

Hunan;

China;

Division of Computational Mathematics and Engineering;

Institute for Computational Science;

Ton Duc Thang University;

Ho Chi Minh City 700000;

Viet Nam;

Faculty of Mathematics and Statistics;

Ton Duc Thang University;

Ho Chi Minh City 700000;

Viet Nam;

展开▼
原文格式 PDF
正文语种 chi
中图分类矿山设计与建设;
关键词
Mine blasting; Rock fragmentation; Artificial intelligence; Hybrid model; Gradient boosting machine; Meta-heuristic algorithm;

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

摘要

著录项

相关主题

期刊订阅