基于人工神经网络(ANN)模型和统计模型的爆破作业背断预测(以gole - gohar铁矿1号矿为例)

IF 1.2 4区工程技术 Q3 MINING & MINERAL PROCESSING

Archives of Mining Sciences Pub Date : 2023-07-20 DOI:10.24425/ams.2022.140705

AbbAS KhAjouEi SirjAni, F. Sereshki, M. Ataei, MohAMMAd AMiri hoSSEini

引用次数: 4

摘要

案例研究

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1)

case study

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来源期刊

Archives of Mining Sciences 工程技术-矿业与矿物加工

CiteScore

2.40

自引率

16.70%

发文量

审稿时长

20 months

期刊介绍： Archives of Mining Sciences (AMS) is concerned with original research, new developments and case studies in mining sciences and energy, civil engineering and environmental engineering. The journal provides an international forum for the publication of high quality research results in: mining technologies, mineral processing, stability of mine workings, mining machine science, ventilation systems, rock mechanics, termodynamics, underground storage of oil and gas, mining and engineering geology, geotechnical engineering, tunnelling, design and construction of tunnels, design and construction on mining areas, mining geodesy, environmental protection in mining, revitalisation of postindustrial areas. Papers are welcomed on all relevant topics and especially on theoretical developments, analytical methods, numerical methods, rock testing, site investigation, and case studies.