TY - GEN
T1 - Analysis of vibration prediction accuracy in underground mining operation based on monitored blast records
AU - Tartibu, L. K.
AU - Okwu, M. O.
AU - Ighawe, D. E.
AU - Mulaba-Bafubiandi, A. F.
N1 - Publisher Copyright:
© 2020 Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The safety and the stability of underground mine openings and sidewalls/pillars are closely related to blast-induced vibrations. A tri-axial transducer can be used to monitor vibration in the transversal, vertical, and longitudinal direction. In this paper, a predictive approach is proposed to estimate the magnitude of vibration. A set of 54 blast vibration recorded from an existing open-pit lignite mine has been considered. The frequency of the vibration, the amount of explosives per delay, the distance between shot points and monitor stations, and the scaled distance are the input parameters considered in this study. The output parameters are made of the particle velocities namely transverse peak, vertical peak, and longitudinal peak. Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference System have been used to predict the blast vibration in Open-pit Lignite Mine. The performance of the proposed approaches has been analysed to measure the prediction accuracy of vibration. The results show that the ANFIS model has a relatively higher level of accuracy as compared to ANN-PSO.
AB - The safety and the stability of underground mine openings and sidewalls/pillars are closely related to blast-induced vibrations. A tri-axial transducer can be used to monitor vibration in the transversal, vertical, and longitudinal direction. In this paper, a predictive approach is proposed to estimate the magnitude of vibration. A set of 54 blast vibration recorded from an existing open-pit lignite mine has been considered. The frequency of the vibration, the amount of explosives per delay, the distance between shot points and monitor stations, and the scaled distance are the input parameters considered in this study. The output parameters are made of the particle velocities namely transverse peak, vertical peak, and longitudinal peak. Artificial Neural Network (ANN) and Adaptive Network-based Fuzzy Inference System have been used to predict the blast vibration in Open-pit Lignite Mine. The performance of the proposed approaches has been analysed to measure the prediction accuracy of vibration. The results show that the ANFIS model has a relatively higher level of accuracy as compared to ANN-PSO.
UR - http://www.scopus.com/inward/record.url?scp=85105790070&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85105790070
T3 - Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics
SP - 987
EP - 999
BT - Proceedings of ISMA 2020 - International Conference on Noise and Vibration Engineering and USD 2020 - International Conference on Uncertainty in Structural Dynamics
A2 - Desmet, W.
A2 - Pluymers, B.
A2 - Moens, D.
A2 - Vandemaele, S.
PB - KU Leuven - Departement Werktuigkunde
T2 - 2020 International Conference on Noise and Vibration Engineering, ISMA 2020 and 2020 International Conference on Uncertainty in Structural Dynamics, USD 2020
Y2 - 7 September 2020 through 9 September 2020
ER -