Masters Theses

Keywords and Phrases

Airblast; Artificial neural network; Empirical equations; Ground vibration; MATLAB®; Rock fragmentation

Abstract

"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.

Advisor(s)

Aouad, Nassib

Committee Member(s)

Ge, Mao Chen
Galecki, Greg

Department(s)

Mining and Nuclear Engineering

Degree Name

M.S. in Mining Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2016

Pagination

ix, 89 pages

Note about bibliography

Includes bibliographic references (pages 82-88).

Rights

© 2016 Raymond Ninnang Tiile, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Blasting
Blast effect -- Measurement
Neural networks (Computer science)

Thesis Number

T 10984

Electronic OCLC #

958294022

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