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

نویسندگانM Esmaeili - M Osanloo- F Rashidinejad - A aghajani Bazzazi
نشریهEngineering with Computers
شماره صفحات549–558
شماره سریال4
شماره مجلد30
ضریب تاثیر (IF)1.951
نوع مقالهFull Paper
تاریخ انتشار2014
رتبه نشریهISI
نوع نشریهچاپی
کشور محل چاپآلمان

چکیده مقاله

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.

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tags: Blasting - Backbreak- ANFIS - ANN - Multiple linear regression