Prediction and Analysis of Blast Parameters Using Artificial Neural Network

Abstract

In this study an attempt is made to predict the ratio of muck pile profile before and after the blast, fly rock and total explosive used, based on simple field tests as well blast design parameters. Prediction is done by making three different artificial neural network (ANN) models. Comparative statistical analysis is made among these three networks to ensure their performance suitability. Models of ANN were based on Feed Forward Back Propagation network with training functions – Resilient Backpropagation, One Step Secant and Powell-Beale Restarts. Total numbers of datasets chosen were 92 among which 17 were chosen for testing and validation and the rest were used for the training of networks. Statistical analysis is also made for these datasets. Considering performance for all the outputs, the best results are predicted by Powell-Beale Restarts, with an average percentage error of 5.871% for the ratio of muck pile before and after the blast, 5.335% for fly rocks and 5.775% for total explosive used. These parameters are predicted by number of holes to be blasted, hole diameter, pattern (spacing (m) X burden (m)), total volume of rock in a blast, average depth and total drill depth.

Superiority of neural networks for pillar stress prediction in bord and pillar method

“…BEM back-analysis was utilized to calculate far-field stress state (Li et al 2009). In this paper, more efficient methods such as neural network-based techniques may be preferred over traditional methods (Jaiswal et al 2004;Lee and Sterling 1992;Leu et al 1998Leu et al , 2001Singh 2002, 2005;Monjezi et al 2006a;Monjezi and Dehghani 2008).…”

Evaluation of effect of blasting pattern parameters on back break using neural networks

“…Similar investigation was performed by Rudajev and Ciz [19]. Monjezi et al [20] predicted the ratio of muck pile before and after the blast, fly rock and total explosive used in the blasting operation. These applications demonstrate that neural network models are efficient in solving problems when many parameters influence the process, and when the process is not fully understood.…”

Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks

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