TY - GEN
T1 - A predictive approach for vibration analysis in underground mining operation
AU - Mulaba-Bafubiandi, A. F.
AU - Tartibu, L. K.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Mine fatalities, accidents and incidents are often associated with ground, roof, stope or side instability. Attenuation of rock integrity or the presence of (under)ground pockets of gases or ground waters lead to the collapse of the tunnel. In this paper, the blast vibration in an Open-pit Lignite Mine has been predicted by incorporating the frequency, the charge per delay, the distance and scaled distance using Artificial Neural Network (ANN). The particle velocities (PPV) namely transverse peak, vertical peak and longitudinal peak are successively the output parameters considered. Particle Swarm Optimization (PSO) was used to train the neural network with 54 experimental and monitored blast records. Results were compared based on correlation between monitored and predicted values of PPV. This study demonstrates the possibility to predict and control blasting effect.
AB - Mine fatalities, accidents and incidents are often associated with ground, roof, stope or side instability. Attenuation of rock integrity or the presence of (under)ground pockets of gases or ground waters lead to the collapse of the tunnel. In this paper, the blast vibration in an Open-pit Lignite Mine has been predicted by incorporating the frequency, the charge per delay, the distance and scaled distance using Artificial Neural Network (ANN). The particle velocities (PPV) namely transverse peak, vertical peak and longitudinal peak are successively the output parameters considered. Particle Swarm Optimization (PSO) was used to train the neural network with 54 experimental and monitored blast records. Results were compared based on correlation between monitored and predicted values of PPV. This study demonstrates the possibility to predict and control blasting effect.
KW - Artificial neural network
KW - Particle swarm optimization
KW - Safe mining
KW - Vibrations
UR - http://www.scopus.com/inward/record.url?scp=85081556441&partnerID=8YFLogxK
U2 - 10.1109/ISCMI47871.2019.9004308
DO - 10.1109/ISCMI47871.2019.9004308
M3 - Conference contribution
AN - SCOPUS:85081556441
T3 - 2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019
SP - 101
EP - 105
BT - 2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019
Y2 - 19 November 2019 through 20 November 2019
ER -