Object's details: 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)

Provider:Czasopisma PAN

Description

Title:
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)
Creator:
Sirjani, Abbas Khajouei;
Sereshki, Farhang;
Ataei, Mohammad;
Hosseini, Mohammad Amiri
Contributor:
Sirjani, Abbas Khajouei : Shahrood University of Technology, Iran;
Sereshki, Farhang : Shahrood University of Technology, Iran;
Ataei, Mohammad : Shahrood University of Technology, Iran;
Hosseini, Mohammad Amiri : Technology Management and Research of Gol-e-gohar, Iran
Description:
Archives of Mining Sciences (AMS) publish research results of wide interest in all fields of mining sciences which include: rock mechanics, mining engineering, mineral processing, geotechnical engineering and tunnelling, mining and engineering geology, minig geodesy and geophysics, ventilation systems, environmental protection in mining and economical aspects in mining. The journal established by the Polish Academy of Sciences, has been regularly issued since 1956. It enable collaboration and exchange of ideas between researchers from different countries as well as provides forum for the publication of high quality papers. Services indexing the journal: Astrophysics Data System (ADS), Baidu Scholar, BazTech, Celdes, Chemical Abstracts Service (CAS), Chemical Abstracts Service (CAS) – SciFinder, CNKI Scholar (China National Knowledge Infrastructure), CNPIEC, EBSCO - TOC Premier, EBSCO Discovery Service, EEVL, Genamics, JournalSeek, GeoRef, Google Scholar, Inspec, J-Gate, JournalTOCs, Naviga (Softweco), PKP, Primo Central (ExLibris), ProQuest (relevant databases), ReadCube, ResearchGate, Summon (Serials Solutions/ProQuest), TDOne (TDNet), TEMA Technik und Management, Thomson Reuters - Journal Citation Reports/Science Edition, Thomson Reuters - Science Citation Index Expanded, U.S. Geological Survey Library, Ulrich's Periodicals Directory/ulrichsweb WorldCat (OCLC). IMPACT FACTOR 2023: 1.2 CiteScore 2023 - 2,40 SNIP 2023 - 0,503 SJR 2023 - 0,313 Score of the Ministry of Science and Higher Education = 100 Submit your article;
Editor-in-Chief Prof. Antoni Tajduś, AGH University of Science and Technology, Krakow Associate Editor Prof. Jakub Siemek, AGH University of Science and Technology, Krakow, Poland Section Editors Dr Katarzyna Cyran, AGH University of Science and Technology, Poland Prof. Wacław Dziurzyński, Strata Mechanics Research Institute, Polish Academy of Sciences, Krakow, Poland Assoc. Prof. Piotr Małkowski, AGH University of Science and Technology, Krakow, Poland Assoc. Prof. Przemysław Skotniczny, Strata Mechanics Research Institute, Polish Academy of Sciences Assoc. Prof. Krzysztof Tajduś, Strata Mechanics Research Institute, Polish Academy of Sciences, Poland Editorial board Prof. Piotr Czaja, AGH University of Science and Technology, Krakow, Poland Prof. Józef Dubiński, Central Mining Institute, Katowice, Poland Prof. Stanisław Nagy, Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie Prof. Stanisław Prusek, Central Mining Institute, Katowice Prof. Tadeusz Słomka, AGH University of Science and Technology, Krakow Prof. Ryszard Tadeusiewicz, AGH University of Science and Technology, Krakow Prof. Wacław Trutwin, Strata Mechanics Research Institute, Polish Academy of Sciences, Krakow Prof. Andrew K. Wojtanowicz, Louisiana State University, Baton Rouge, USA Prof. Tao Xu, Northeastern University, China Chairman of International Advisory Board Prof. Marek Cała, AGH University of Science and Technology, Krakow, Poland Members of International Advisory Board Prof. Leandro R. Alejano, Universidad de Vigo, Spain Prof. Kashy Aminian, West Virginia University, USA Prof. Timothy Carr, West Virginia University, USA Prof. Eleonora Widzyk-Capehart, University of Chile, Chile Prof. Pedro Riesgo Fernández, University of Oviedo, Spain Prof. Mihaly Dobróka, University of Miskolc, Hungary Prof. Sevket Durucan, Imperial College London, United Kingdom Prof. Aidarkhan Kaltayev, al-Frabi Kazakh State University, Almaty Kazachstan Prof. Evgeny I. Križanivskij, National Oil and Gas University of Ukraine, Ivanofrankovsk, Ukraine Prof. Ian Lowndes, University of Nottingham, Nottingham, United Kingdom Prof. Henryk Marcak, AGH University of Science and Technology, Krakow Prof. Marian Marschalko, VŠB-Technical University of Ostrava,Czech Republic Prof. Stefan Miska, University of Tulsa, Tulsa, USA Prof. Pierpaolo Oreste, Politecnico di Torino, Italy Prof. Durga Charan Panigrahi, Indian School of Mines, Dhanbad, India Prof. Tadeusz Patzek, The University of Texas at Austin, USA Prof. Lucjan Pawłowski, University of Technology, Lublin Prof. Genadyi G. Pivnyak, National Mining University of Ukraine, Dniepropetrovsk, Ukraine Prof. Pekka Särkkä, Helsinki University of Technology Helsinki, Finland Prof. Anton Sroka, Strata Mechanics Research Institute of the Polish Academy of Sciences, Krakow Prof. Stanisław Stryczek, AGH University of Science and Technology, Krakow Prof. Vlad Ulmanu, University Petroleum-Gas of Ploiesti, Romania Prof. Jann Rune Ursin, University of Stavanger, Norway Prof. Jan Wachowicz, Central Mining Institute, Katowice Prof. Yaroslavl Vasyuchkov, Russian Academy of Natural Sciences, Moscow, Russia Prof. Isik Yilmaz, Cumhuriyet University Sivas, Turkey
Object availability:
free access
Rights:
Archives of Mining Sciences jest czasopismem wydawanym w wolnym dostępie na licencji CC BY-NC 4.0. <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en">https://creativecommons.org/licenses/by-nc/4.0/deed.en</a>Archives of Mining Sciences is an open access journal with all content available with no charge in full text version. The journal content is available under the licencse CC BY-NC 4.0 <a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en">https://creativecommons.org/licenses/by-nc/4.0/deed.en</a>.
Date:
2022.03.28
Type:
Article
Coverage:
107-121
Publisher:
Committee of Mining PAS
Subject:
blasting;
Backbreak;
artificial neural network;
statistical models;
Gol-e-gohar iron ore mine
Identifier:
ISSN 0860-7001
Data provider:
Czasopisma PAN

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Object's details: 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)

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