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dc.contributor.authorCastillo Sequera, José Luis 
dc.contributor.authorRosales Huamaní, Jimmy Aurelio
dc.contributor.authorSaenz Pérez Alvarado, Roberth
dc.contributor.authorRojas Villanueva, Uwe
dc.date.accessioned2020-10-01T09:20:32Z
dc.date.available2020-10-01T09:20:32Z
dc.date.issued2020-08-24
dc.identifier.bibliographicCitationRosales Huamani, J.A., Saenz Perez-Alvarado, R., Rojas Villanueva, U. & Castillo Sequera, J.L., 2020, "Design of a predictive model of a rock breakage by blasting using artificial neural networks", Symmetry, vol. 12, no. 9, pp. 1405-1421
dc.identifier.issn2073-8994
dc.identifier.urihttp://hdl.handle.net/10017/44507
dc.description.abstractOver the years, various models have been developed in the stages of the mining process that have allowed predicting and enhancing results, but it is the breakage, the variable that connects all the activities of the mining process from the point of view of costs (drilling, blasting, loading, hauling, crushing and grinding). To improve this process, we have designed and developed a computational model based on an Artificial Neural Network (ANN), the same that was built using the most representative variables such as the properties of explosives, the geomechanical parameters of the rock mass, and the design parameters of drill-blasting. For the training and validation of the model, we have taken the data from a copper mine as reference located in the north of Chile. The ANN architecture was of the supervised type containing: an input layer, a hidden layer with 13 neurons and an output layer that includes the sigmoid activation function with symmetrical properties for optimal model convergence. The ANN model was fed-back in its learning with training data until it becomes perfected, and due to the experimental results obtained, it is a valid prediction option that can be used in future blasting of ore deposits with similar characteristics using the same representative variables considered. Therefore, it constitutes a valid alternative for predicting rock breakage, given that it has been experimentally validated, with moderately reliable results, providing higher correlation coefficients than traditional models used, and with the additional advantage that an ANN model provides, due to its ability to learn and recognize collected data patterns. In this way, using this computer model we can obtain satisfactory results that allow us to predict breakage in similar scenarios, providing an alternative for evaluating the costs that this entails as a contribution to the work.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPI
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectArtificial neural networksen
dc.subjectRock breakageen
dc.subjectRock blastingen
dc.titleDesign of a predictive model of a rock breakage by blasting using artificial neural networksen
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaInformáticaes_ES
dc.subject.ecienciaComputer scienceen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Ciencias de la Computaciónes_ES
dc.date.updated2020-10-01T09:13:00Z
dc.relation.publisherversionhttps://doi.org/10.3390/sym12091405
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/sym12091405
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiAR/0000034774
dc.identifier.publicationtitleSymmetry
dc.identifier.publicationvolume12
dc.identifier.publicationlastpage1421
dc.identifier.publicationissue9
dc.identifier.publicationfirstpage1405


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