Multiple regression, ANN and ANFIS models for prediction of backbreak in the open pit blasting

Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and ex...

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Bibliographic Details
Published in:Engineering with computers 2014-10, Vol.30 (4), p.549-558
Main Authors: Esmaeili, Mohammad, Osanloo, Morteza, Rashidinejad, Farshad, Aghajani Bazzazi, Abbas, Taji, Mohammad
Format: Article
Language:English
Subjects:
Adaptive systems
Artificial neural networks
Blasting (explosive)
CAE) and Design
Calculus of Variations and Optimal Control
Optimization
Classical Mechanics
Computer Science
Computer-Aided Engineering (CAD
Control
Fuzzy logic
Learning theory
Math. Applications in Chemistry
Mathematical and Computational Engineering
Mathematical models
Neural networks
Original Article
Regression
Systems Theory
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Summary:Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. Backbreak can be affected by various parameters such as the rock mass properties, blasting geometry and explosive properties. In this study, the application of the artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS) for prediction of backbreak, was described and compared with the traditional statistical model of multiple regression. The performance of these models was assessed through the root mean square error, correlation coefficient ( R 2 ) and mean absolute percentage error. As a result, it was found that the constructed ANFIS exhibited a higher performance than the ANN and multiple regression for backbreak prediction.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-012-0298-2