Several non-linear models in estimating air-overpressure resulting from mine blasting. (July 2016)

• Record Type: Journal Article
• Authors: Hasanipanah, Mahdi; Jahed Armaghani, Danial; Khamesi, Hossein; Bakhshandeh Amnieh, Hassan; Ghoraba, Saber
• Is Part Of: Engineering with computers. Volume 32:Number 3(2016)
• Page Start: 441
• Page End: 455
• Publication Date: 2016-07
• Subjects: Blasting environmental impact -- AOp -- ANN -- Fuzzy system -- ANFIS; Engineering design -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Conception technique -- Informatique -- Périodiques; Conception assistée par ordinateur -- Périodiques; Electronic journals; 620.00285
• DOI: 10.1007/s00366-015-0425-y
• Languages: English
• ISSNs: 0177-0667

Abstract

Abstract This research presents several non-linear models including empirical, artificial neural network (ANN), fuzzy system and adaptive neuro-fuzzy inference system (ANFIS) to estimate air-overpressure (AOp) resulting from mine blasting. For this purpose, Miduk copper mine, Iran was investigated and results of 77 blasting works were recorded to be utilized for AOp prediction. In the modeling procedure of this study, results of distance from the blast-face and maximum charge per delay were considered as predictors. After constructing the non-linear models, several performance prediction indices, i.e. root mean squared error (RMSE), variance account for (VAF), and coefficient of determination (R 2 ) and total ranking method are examined to choose the best predictive models and evaluation of the obtained results. It is obtained that the ANFIS model is superior to other utilized techniques in terms ofR 2, RMSE, VAF and ranking herein. As an example, RMSE values of 5.628, 3.937, 3.619 and 2.329 were obtained for testing datasets of empirical, ANN, fuzzy and ANFIS models, respectively, which indicate higher performance capacity of the ANFIS technique to estimate AOp compared to other implemented methods.