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Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

التفاصيل البيبلوغرافية
العنوان: Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
المؤلفون: Prashanth Ragam, Hoang Nguyen, Trung Nguyen-Thoi, Xuan-Nam Bui, Hossein Moayedi
المصدر: Applied Sciences, Vol 9, Iss 21, p 4554 (2019)
Applied Sciences
Volume 9
Issue 21
بيانات النشر: MDPI AG, 2019.
سنة النشر: 2019
مصطلحات موضوعية: Correlation coefficient, Mean squared error, Computer science, eanns, Computer Science::Neural and Evolutionary Computation, Open-pit mining, 02 engineering and technology, mining, 010502 geochemistry & geophysics, Machine learning, computer.software_genre, 01 natural sciences, lcsh:Technology, Physics::Geophysics, Set (abstract data type), lcsh:Chemistry, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, bench blasting, Instrumentation, ann, lcsh:QH301-705.5, 0105 earth and related environmental sciences, Fluid Flow and Transfer Processes, Artificial neural network, business.industry, lcsh:T, Process Chemistry and Technology, General Engineering, Variance (accounting), ensemble technique, artificial intelligence, lcsh:QC1-999, Computer Science Applications, Mean absolute percentage error, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 020201 artificial intelligence & image processing, State (computer science), Artificial intelligence, business, fly-rock, lcsh:Engineering (General). Civil engineering (General), computer, lcsh:Physics
الوصف: Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models)
210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first
then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.
وصف الملف: application/pdf
اللغة: English
تدمد: 2076-3417
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95e87375037539f0e7ecbf28bd8829cb
https://www.mdpi.com/2076-3417/9/21/4554
Rights: OPEN
رقم الانضمام: edsair.doi.dedup.....95e87375037539f0e7ecbf28bd8829cb
قاعدة البيانات: OpenAIRE
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