Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

Serdar Ercins

doi:10.46460/ijiea.978343

EN TR

Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

Abstract

In this study, ground vibrations caused by blasting applications in a quarry were recorded and these values were evaluated and estimated by using an artificial neural network (ANN) model. Of the 28 vibration data measured, 20 were used for ANN training, 4 for validation and the remaining 4 for testing. In the model, peak particle velocity (PPV) was used as the output parameter, and the maximum explosive amount per delay and scaled distance were used as input parameters. In addition, MAPE, RMSE and R2 performance criteria were calculated from the realized, predicted by ANN and PPV values obtained from the field equation. The maximum amount of explosives used per delay and the sensitivity analysis of the scaled distance on the highest particle velocity were also determined. As a result, when the vibration data calculated from the field equation and estimated from the ANN model were compared with the realized vibration data, it was seen that the values obtained by the ANN model had a higher correlation.

Keywords

Artificial neural network , vibration , blasting , quarry , sensitivity analysis , MAPE , RMSE , R2

Patlama Kaynaklı Yer Titreşiminin YSA ile Tahmini ve Tahmin Performansı

Öz

Bu çalışmada bir taş ocağında gerçekleştirilen patlatma uygulamalarından kaynaklanan yer titreşimleri kaydedilmiş ve bu değerler yapay sinir ağı (YSA) modeli kullanılarak değerlendirilmiş ve tahmin edilmiştir. Ölçümü alınan 28 titreşim verisinin 20 tanesi YSA’nın eğitimi, 4’ü doğrulama ve geriye kalan 4’ü de test için kullanılmıştır. Modelde çıktı parametresi olarak PPV, girdi parametresi olarak ise gecikme başına en fazla patlayıcı miktarı ve ölçekli mesafe kullanılmıştır. Ayrıca MAPE, RMSE ve R2 performans kriterleri, gerçekleşen, YSA ile tahmin edilen ve saha denkleminden elde edilen PPV değerlerinden hesaplanmıştır. Gecikme başına kullanılan en fazla patlayıcı madde miktarı ve ölçekli mesafenin, en yüksek parçacık hızı üzerindeki duyarlılık analizi de belirlenmiştir. Sonuçta, saha denkleminden hesaplanan ve YSA modelinden tahmin edilen titreşim verileri, gerçekleşen titreşim verileri ile karşılaştırıldığında, YSA modeli ile elde edilen değerlerin daha yüksek korelasyona sahip olduğu görülmüştür.

Anahtar Kelimeler

Yapay sinir ağı , titreşim , patlatma , taş ocağı , duyarlılık analizi , MAPE , RMSE , R2

References

1. [1] Özyurt, M.C., (2018), “The Investigation of Using Artificial Neural Networks and Game Theory on Underground Mining Method Selection”, Ph.D. Dissertation, Istanbul University, Istanbul, Turkey.
2. [2] Khandelwal M, Singh TN., (2005), “Prediction of Blast Induced Air Overpressure in Opencast Mine”, Noise & Vibration Worldwide, 36(2), 7-16.
3. [3] Liu, L., (2014), “Modeling and Evaluation of the Safety Control Capability of coal Mine Based on System Safety”, Journal of Cleaner Production, 84(2014), 797-802.
4. [4] Özer, Ü., Karadoğan, A., Özyurt, M.C., Şahinoğlu Ü.K., Sertabipoğlu, S., (2019), “Environmentally Sensitive Blasting Design Based on Risk Analysis by Using Artificial Neural Networks”, Arabian Journal of Geosciences, 12(2), 2-13.
5. [5] Tawadrous AS., (2006), “Evaluation of Artificial Neural Networks as a Reliable Tool in Blast Design”, International Society of Explosives Engineers; (1) 1–12.
6. [6] Khandelwal M, Singh TN., (2007), “Evaluation of Blast Induced Ground Vibration Predictors”, Soil Dynamics and Earthquake Engineering., 27(2), 116–25.
7. [7] Khandelwal M, Singh TN., (2006), “Prediction of Blast Induced Ground Vibrations and Frequency in Opencast Mine: A Neural Network Approach”, Journal of Sound Vibration, 289(4-5), 711–725.
8. [8] Mohamed MT., (2009), “Artificial Neural Network for Prediction and Control of Blasting Vibrations in Assiut (Egypt) Limestone Quarry”, International Journal of Rock Mechanics and Mining Sciences, 46(2), 426–431.
9. [9] Chapra, S.C., Canale, R.P., (2015), “Software and Numerical Methods for Engineers with Programming Applications”, Literature Publishing, Translators: Hasan Heperkan, Uğur Kesgin, Istanbul, Turkey.
10. [10] Öztemel, E., (2016), Artificial Neural Networks, (4th Edition), Daisy Publishing, Istanbul, Turkey.

11. [11] Khandelwal M. and T.N. Singh, (2009), “Prediction of Blast-Induced Ground Vibration Using Artificial Neural Network”, International Journal of Rock Mechanics & Mining Sciences, 46(7), 1215-1216.
12. [12] Maulenkamp F, Grima MA., (1999), “Application of Neural Networks for The Prediction of The Unconfined Compressive Strength (Ucs) From Equotip Hardness”, International Journal of Rock Mechanics and Mining Sciences, 36(1), 29-39.
13. [13] Lv, C., Yang Xing, Junzhi Zhang, Xiaoxiang Na, Yutong Li, Teng Liu, Dongpu Cao, Wang, F. Y. (2017), “Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System”, IEEE Transactions on Industrial Informatics, 14(8), 3436-3446.
14. [14] Baghirli, O., (2015), “Comparison of Lavenberg Marquardt, Scaled Conjugate Gradient and Bayes Regularization Backpropagation Algorithms for Multistep Ahead Wind Speed Forecasting Using Multilayer Perceptron Feedforward Neural Network”, Master Thesis, Uppsala University Department of Earth Sciences, Campus Gotland, Sweden.
15. [15] Siskind, D. E., Stagg, M. S., Kopp, J. W., Dowding, C. H., (1980), “Structure Response and Damage Produced by Ground Vibration from Surface Mine Blasting”, No: 8507, Bureau of Mines, United States Department of Interior, Washington.
16. [16] Dowding C. H. (1985), Blast Vibration Monitoring and Control, Prentice-Hall, 297s.
17. [17] İnan. S., Öztürk, A. ve Gürsoy, H., (1993), Stratigraphy of the Ulaş-Sincan (Sivas) region, Turkish Journal of Earth Sciences, 2, 1-15.
18. [18] Uyar G., Aksoy C. (2019), “Comparative Review and Interpretation of the Conventional and New Methods In Blast Vibration Analyses”, Geomechanics and Engineering, 18(5), 545-554.
19. [19] V. Yadav and S. Nath, (2017), “Forecasting of Pm10 Using Autoregressive Models and Exponential Smoothing Technique”, Asian Journal of Water, Environment and Pollution, 14(4), 109– 113.
20. [20] Duvall WI, Johnson CF, Meyer AVC (1963), “Vibrations from Instantaneous and Millisecond Delay Quarry Blasts”, No. 6151, US, Bureau of Mines, United States Department of Interior, Washington.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Serdar Ercins ^*
0000-0001-8730-4135
Türkiye

Publication Date

December 31, 2021

Submission Date

August 3, 2021

Acceptance Date

December 10, 2021

Published in Issue

Year 2021 Volume: 5 Number: 2

DOI

https://doi.org/10.46460/ijiea.978343

IZ

https://izlik.org/JA93KL59FA

APA

Ercins, S. (2021). Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance. International Journal of Innovative Engineering Applications, 5(2), 205-211. https://doi.org/10.46460/ijiea.978343

AMA

1.Ercins S. Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance. IJIEA. 2021;5(2):205-211. doi:10.46460/ijiea.978343

Chicago

Ercins, Serdar. 2021. “Prediction of Blast-Induced Ground Vibration With ANN and Prediction Performance”. International Journal of Innovative Engineering Applications 5 (2): 205-11. https://doi.org/10.46460/ijiea.978343.

EndNote

Ercins S (December 1, 2021) Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance. International Journal of Innovative Engineering Applications 5 2 205–211.

IEEE

[1]S. Ercins, “Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance”, IJIEA, vol. 5, no. 2, pp. 205–211, Dec. 2021, doi: 10.46460/ijiea.978343.

ISNAD

Ercins, Serdar. “Prediction of Blast-Induced Ground Vibration With ANN and Prediction Performance”. International Journal of Innovative Engineering Applications 5/2 (December 1, 2021): 205-211. https://doi.org/10.46460/ijiea.978343.

JAMA

1.Ercins S. Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance. IJIEA. 2021;5:205–211.

MLA

Ercins, Serdar. “Prediction of Blast-Induced Ground Vibration With ANN and Prediction Performance”. International Journal of Innovative Engineering Applications, vol. 5, no. 2, Dec. 2021, pp. 205-11, doi:10.46460/ijiea.978343.

Vancouver

1.Serdar Ercins. Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance. IJIEA. 2021 Dec. 1;5(2):205-11. doi:10.46460/ijiea.978343

This work is licensed under CC BY-NC 4.0

Prediction of Blast-Induced Ground Vibration with ANN and Prediction Performance

Abstract

Keywords

Patlama Kaynaklı Yer Titreşiminin YSA ile Tahmini ve Tahmin Performansı

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite