Keywords and Phrases
Airblast; Artificial neural network; Empirical equations; Ground vibration; MATLAB®; Rock fragmentation
"Blasting has been widely used as an economical and cheap way of rock breakage in mining and civil engineering applications. An optimal blast yields the best fragmentation in a safe, economic and environmentally friendly manner. The degree of fragmentation is vital as it determines to a large extent the utilization of equipment, productivity and mill throughput. Explosive energy, besides rock fragmentation, creates health and safety issues such as ground vibration, air blast, fly rock, and back breaks among others. As a result, the explosive energy impacts structures and buildings located in the vicinity of the blasting operation, and causes human annoyance, as well as exposes operators in the field to hazardous conditions. There is therefore a need to develop a model to predict blast-induced ground vibration (PPV), airblast (AOp), and rock fragmentation. Artificial neural network (ANN) technique is preferred over empirical and other statistical predictive methods as it is able to incorporate the numerous factors affecting the outcome of a blast. This study seeks to develop a simultaneous integrated prediction model for rock fragmentation, ground vibration and air blast using MATLAB-based artificial neural network system. Training, validation and testing was done with a total of 180 monitored blast records taken from a gold mining company in Ghana using a three-layer, feed-forward back-propagation ANN.
Based on the results obtained from the study, ANN model with architecture of 7-13-3 was found optimum having the least root mean square error (RMSE) of 0.307. Artificial neural network (ANN) technique has been compared to empirical and conventional statistical methods. Sensitivity analysis has also been conducted to ascertain the relative influence of each input parameter on rock fragmentation, PPV and AOp"--Abstract, page iii.
Ge, Mao Chen
Mining and Nuclear Engineering
M.S. in Mining Engineering
Missouri University of Science and Technology
ix, 89 pages
© 2016 Raymond Ninnang Tiile, All rights reserved.
Thesis - Open Access
Blast effect -- Measurement
Neural networks (Computer science)
Electronic OCLC #
Tiile, Raymond Ninnang, "Artificial neural network approach to predict blast-induced ground vibration, airblast and rock fragmentation" (2016). Masters Theses. 7571.