An ANN-Fuzzy Cognitive Map-Based Z-Number Theory to Predict Flyrock Induced by Blasting in Open-Pit Mines

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aut.relation.journalRock Mechanics and Rock Engineeringen_NZ
aut.researcherKalatehjari, Roohollah
dc.contributor.authorHosseini, Sen_NZ
dc.contributor.authorPoormirzaee, Ren_NZ
dc.contributor.authorHajihassani, Men_NZ
dc.contributor.authorKalatehjari, Ren_NZ
dc.date.accessioned2022-04-28T01:47:28Z
dc.date.available2022-04-28T01:47:28Z
dc.description.abstractBlasting is widely employed as an accepted mechanism for rock breakage in mining and civil activities. As an environmental side effect of blasting, flyrock should be investigated precisely in open-pit mining operations. This paper proposes a novel integration of artificial neural network and fuzzy cognitive map (FCM) with Z-number reliability information to predict flyrock distance in open-pit mine blasting. The developed model is called the artificial causality-weighted neural networks, based on reliability (ACWNNsR). The reliability information of Z-numbers is used to eliminate uncertainty in expert opinions required for the initial matrix of FCM, which is one of the main advantages of this method. FCM calculates weights of input neurons using the integration of nonlinear Hebbian and differential evolution algorithms. Burden, stemming, spacing, powder factor, and charge per delay are used as the input parameters, and flyrock distance is the output parameter. Four hundred sixteen recorded basting rounds are used from a real large-scale lead–zinc mine to design the architecture of the models. The performance of the proposed ACWNNsR model is compared with the Bayesian regularized neural network and multilayer perceptron neural network and is proven to result in more accurate prediction in estimating blast-induced flyrock distance. In addition, the results of a sensitivity analysis conducted on effective parameters determined the spacing as the most significant parameter in controlling flyrock distance. Based on the type of datasets used in this study, the presented model is recommended for flyrock distance prediction in surface mines where buildings are close to the blasting site.en_NZ
dc.identifier.citationRock Mechanics and Rock Engineering (2022). https://doi.org/10.1007/s00603-022-02866-z
dc.identifier.doi10.1007/s00603-022-02866-zen_NZ
dc.identifier.issn0723-2632en_NZ
dc.identifier.issn1434-453Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15087
dc.languageenen_NZ
dc.publisherSpringer Science and Business Media LLCen_NZ
dc.relation.urihttps://link.springer.com/article/10.1007/s00603-022-02866-z
dc.rightsOpen Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectFlyrock; Blasting; Open-pit mining; FCM; Z-number; ANN
dc.titleAn ANN-Fuzzy Cognitive Map-Based Z-Number Theory to Predict Flyrock Induced by Blasting in Open-Pit Minesen_NZ
dc.typeJournal Article
pubs.elements-id453695
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Future Environments
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