Articles

Signal processing application for vibration generated by blasting in tunnels

User Rating:  / 0
PoorBest 

Authors:


S.Feltane, orcid.org/0000-0003-3521-575X, Laboratory of Natural Resources and Development Badji Mokhtar University, Annaba, Algeria, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.Yahyaoui, orcid.org/0000-0002-9278-7562, National Polytechnic School, Alger, Algeria

A.Hafsaoui, orcid.org/0000-0002-1720-9527, Laboratory of Natural Resources and Development Badji Mokhtar University, Annaba, Algeria, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.Boussaid, orcid.org/0000-0002-6859-9983, University of brothers Mentouri, Constantine 1, Algeria


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2021, (5): 054 - 060

https://doi.org/10.33271/nvngu/2021-5/054



Abstract:



Purpose.
To study the vibrations waves generated by blasting in a tunnel using the signal processing tools.


Methodology.
Field tests are carried out to measure vibration wave during blasting operations at different locations in the tunnel and its immediate environment. Results of the measurements are processed by the autocorrelation method, which consists of filtering based on signal shape recognition. A comparison is accomplished between the peak particle velocities (PPV) measured and those obtained after filtering.


Findings.
The results obtained after filtering gave a significant reduction in PPV of the measured vibration amplitudes in comparison to those obtained after treatment for the three components: longitudinal, transversal and vertical ones. Good knowledge of vibration source is important for amplitude attenuation regarding the observed difference between the recorded seismogram during explosion of a single unit charge and other standard explosions.


Originality.
The work introduces signal processing methods for filtering vibration signals related to blasting, which is insufficiently studied.


Practical value.
This study shows that the treatment of blasting vibrations by a filtering method should reduce the peak velocity of the particles by separating the signals and eliminating the interference in the initial signal.



Keywords:
blasting, vibration amplitude, signal processing, autocorrelation methods, filtered signal, signal degradation

References.


1. Ragam, P., & Nimaje, D. (2018). Assessment of Blast-induced Ground Vibration using Different Predictor Approaches A Comparison. Chemical Engineering Transactions, 66, 487-492. https://doi.org/10.3303/CET1866082.

2. Aloui, M., Bleuzen, Y., Essefi, E., & Abbes, C. (2016). Ground vibrations and air blast effects induced by blasting in open pit mines: Case of Metlaoui Mining Basin, South western Tunisia. Journal of Geology and Geophysics,5(3).

3. Ray, S., & Dauji, S. (2019). Ground vibration attenuation relationship for underground blast: a case study. Journal of The Institution of Engineers (India): Series A, 100(4). https://doi.org/10.1007/s40030-019-00382-y.

4. Mansouri, H., & Farsangi Ebrahimi, M.A. (2015). Blast vibration modeling using linear superposition method. Journal of Mining and Environment, 6(2).

5. Chen, S.H., Wu, J., & Zhang, Z.H. (2015). Influence of millisecond time, charge length and detonation velocity on blasting vibration. Journal of Central South University, 22(12). https://doi.org/10.1007/s11771-015-3030-8.

6. Dhion, P., Chani, P., & Syarif, N. (2019), Linier Superposition Analysis on Managing Blasting Ground Vibration in Coal Mining. Indonesian Mining Professionals Journal. https://doi.org/10.36986/impj.v1i1.8.

7. Kumar, S., Mishra, A.K., Choudhary, B.S., Sinha, R.K., Deepak,D., & Agrawal, H. (2020). Prediction of ground vibration induced due to single hole blast using explicit dynamics. Mining, Metallurgy and Exploration, 37, 733-741.

8. Trivedi, R., Singh, T.N., Mudgal, K., & Gupta, N. (2014). Application of artificial neural network for blast performance evaluation.International Journal of Research in Engineering and Technology,3(5), 564-574.

9. Ragam, P., & Nimaje, D.S. (2018). Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: Acase study.Noise & Vibration Worldwide, 49(3), 111-119.

10. Kumar, N., Mishra, B., & Bali, V. (2018). A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network. In Tiwari, B., Tiwari, V., Das,K., Mishra, D., & Bansal, J. (Eds.). Proceedings of International Conference on Recent Advancement on Computer and Communication. Lecture Notes in Networks and Systems, 34. Springer. https://doi.org/10.1007/978-981-10-8198-9_3.

11. Farahani, G. (2017). Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2017(1), 1-16. https://doi.org/10.1186/s13636-017-0110-8.

12. Tryputen, M., Kuznetsov, V., Kuznetsova, A., Maksim, K., & Tryputen, M. (2020, September). Developing Stochastic Model of a Workshop Power Grid. In2020 IEEE Problems of Automated Electrodrive. Theory and Practice (PAEP),(pp. 1-6). IEEE. https://doi.org/10.1109/PAEP49887.2200.9240898.

 

Visitors

7575105
Today
This Month
All days
1332
97591
7575105

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home