ARTIFICIAL NEURAL NETWORK EVALUATION AND PREDICTION OF BLAST-INDUCED PEAK PARTICLE VELOCITY - A CASE STUDY OF LIMESTONE MINING

Authors

  • Rizqi Prastowo Institut Teknologi Nasional Yogyakarta
  • Hendro Purnomo Institut Teknologi Nasional Yogyakarta
  • Firhad Firmansyah Institut Teknologi Nasional Yogyakarta
  • Vico Luthfi Ipmawan Department of Physics, Institut Teknologi Sumatera

DOI:

https://doi.org/10.30556/imj.Vol27.No1.2024.1531

Keywords:

peak particle velocity, blast-induced ground vibration, artificial neural network, conventional predictors

Abstract

In recent decades, generation of ground vibrations results from blasting activities in mining sector has been identified as a significant cause of extensive harm to nearby structures, vegetation, and individuals. Hence, it is imperative to closely monitor and accurately forecast the uncertain levels of vibration, and implement the appropriate steps to mitigate their potentially harmful impact. The objective of this study was to establish a correlation between the peak particle velocity and the various parameters that influence it. This study employed the deployment of the artificial neural network approach to assess and forecast the uncertain ground vibrations. In this study, a multilayer perception neural network with three layers and a feed-forward back-propagation architecture was employed. The network consisted of five input parameters, namely the distance from the blast face, maximum charge per delay, spacing, burden, and depth hole. The output of interest was the peak particle velocity. The neural network was trained using the Levenberg–Marquardt algorithm, and the training dataset comprised 29 experimental records and blast event data obtained from the limestone mine in Indonesia. In order to assess the effectiveness and the precision of the artificial neural network model that was created, a total of four conventional predictor models were utilized. These models were proposed by reputable sources such as the US Bureau of Mines, Ambraseys–Hendron, Langefors–Kihlstrom, and the Bureau of Indian Standards. The results collected from the demonstrate study show that the artificial neural network model suggested in this research has the ability to provide more precise estimations of ground vibrations in comparison to existing conventional prediction models. The artificial neural network model yielded a coefficient of determination (R2) of 0.9332 and a root mean square error (RMSE) of 0.4763.

References

Abd Elwahab, A., Topal, E. and Jang, H.D. (2023) ‘Review of machine learning application in mine blasting’, Arabian Journal of Geosciences, 16(2). Available at: https://doi.org/10.1007/s12517-023-11237-z.

Ambraseys, N.N. and Hendron, A.J. (1968) Dynamic behaviour of rock masses. J. Wiley & Sons.

Armaghani, D.J., Hajihassani, M., Mohamad, E.T., Marto, A. and Noorani, S.A. (2014) ‘Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization’, Arabian Journal of Geosciences, 7(12), pp. 5383–5396. Available at: https://doi.org/10.1007/s12517-013-1174-0.

Ataei, M. and Sereshki, F. (2017) ‘Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm’, Journal of Mining & Environment, 8(2), pp. 291–304. Available at: https://doi.org/10.22044/jme.2016.654.

Bakhshandeh Amnieh, H., Siamaki, A. and Soltani, S. (2012) ‘Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach’, Safety Science, 50(9), pp. 1913–1916. Available at: https://doi.org/10.1016/j.ssci.2012.05.008.

Bui, X.N., Nguyen, H. and Nguyen, T.A. (2021) ‘Artificial Neural Network Optimized by Modified Particle Swarm Optimization for Predicting Peak Particle Velocity Induced by Blasting Operations in Open Pit Mines’, Inzynieria Mineralna, 1(2), pp. 79–90. Available at: https://doi.org/10.29227/IM-2021-02-07.

Bui, X.N., Nguyen, H., Tran, Q.H., Nguyen, D.A. and Bui, H.B. (2021) ‘Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization’, Natural Resources Research, 30(3), pp. 2663–2685. Available at: https://doi.org/10.1007/s11053-021-09823-7.

Chen, G. and Huang, S.L. (2001) ‘Analysis of ground vibrations caused by open pit production blasts - A case study’, Fragblast, 5(1–2), pp. 91–107. Available at: https://doi.org/10.1076/frag.5.1.91.3316.

Das, A. and Chakrabortty, P. (2021) ‘Artificial neural network and regression models for prediction of free-field ground vibration parameters induced from vibroflotation’, Soil Dynamics and Earthquake Engineering, 148(November 2020), p. 106823. Available at: https://doi.org/10.1016/j.soildyn.2021.106823.

Dindarloo, S.R. (2015) ‘6. Peak particle velocity prediction using support vector machines A surface blasting case study. S.R. Dindarloo Department of Mining and Nuclear Engineering, Missouri University of Science and Technology, Bella, MO, USA.pdf’, 115(JULY), pp. 637–643.

Duvall, W.I. and Petkof, B. (1959) Spherical propagation of explosion-generated strain pulses in rock. Washington DC: US Bureau of Mines.

Hajihassani, M., Jahed Armaghani, D., Marto, A. and Tonnizam Mohamad, E. (2015) ‘Vibrations au sol prédiction dans quarry dynamitage à travers un réseau neural artificiel optimisé par une concurrence impérialiste algorithme’, Bulletin of Engineering Geology and the Environment, 74(3), pp. 873–886. Available at: https://doi.org/10.1007/s10064-014-0657-x.

Hajihassani, M., Jahed Armaghani, D., Monjezi, M., Mohamad, E.T. and Marto, A. (2015) ‘Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach’, Environmental Earth Sciences, 74(4), pp. 2799–2817. Available at: https://doi.org/10.1007/s12665-015-4274-1.

Indian Standard Institute (ISI) (1973) Criteria for safety and design of structures subjected to under ground blast, ISI Bulletin.

Khandelwal, M. and Singh, T.N. (2009) ‘Prediction of blast-induced ground vibration using artificial neural network’, International Journal of Rock Mechanics and Mining Sciences, 46(7), pp. 1214–1222. Available at: https://doi.org/10.1016/j.ijrmms.2009.03.004.

Langefors, U. and Kihlstrom, B. (1963) The modern technique of rock blasting. New York: Wiley.

Lawal, A.I. and Kwon, S. (2021) ‘Application of artificial intelligence to rock mechanics: An overview’, Journal of Rock Mechanics and Geotechnical Engineering, 13(1), pp. 248–266. Available at: https://doi.org/10.1016/j.jrmge.2020.05.010.

Monjezi, M., Ahmadi, M., Sheikhan, M., Bahrami, A. and Salimi, A.R. (2010) ‘Predicting blast-induced ground vibration using various types of neural networks’, Soil Dynamics and Earthquake Engineering, 30(11), pp. 1233–1236. Available at: https://doi.org/10.1016/j.soildyn.2010.05.005.

Pal Roy, P. (2021) ‘Emerging trends in drilling and blasting technology: concerns and commitments’, Arabian Journal of Geosciences, 14(7). Available at: https://doi.org/10.1007/s12517-021-06949-z.

Prashanth, R. and Nimaje, D.S. (2018) ‘Estimation of peak particle velocity using soft computing technique approaches: a review’, Noise and Vibration Worldwide, 49(9–10), pp. 302–310. Available at: https://doi.org/10.1177/0957456518799536.

Priyadarshi, V., Paswan, R.K., Rana, V.S., Kushwaha, S. and Roy, M.P. (2023) ‘Evaluation and Quantification of Textural Properties of a Rock and its Impact on Blast Induced Ground Vibrations’, in Asian Mining Congress. Springer, pp. 452–466.

Ragam, P. and Nimaje, Devidas Sahebraoji (2018) ‘Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: A case study’, Noise and Vibration Worldwide, 49(3), pp. 111–119. Available at: https://doi.org/10.1177/0957456518763161.

Ragam, P. and Nimaje, Devidas S. (2018) ‘Monitoring of blast-induced ground vibration using WSN and prediction with an ANN approach of ACC dungri limestone mine, India’, Journal of Vibroengineering, 20(2), pp. 1051–1062. Available at: https://doi.org/10.21595/jve.2017.18647.

Ram Chandar, K., Sastry, V.R. and Hegde, C. (2017) ‘A critical comparison of regression models and artificial neural networks to predict ground vibrations’, Geotechnical and Geological Engineering, 35(2), pp. 573–583. Available at: https://doi.org/10.1007/s10706-016-0126-3.

Shirani Faradonbeh, R., Jahed Armaghani, D., Abd Majid, M.Z., MD Tahir, M., Ramesh Murlidhar, B., Monjezi, M. and Wong, H.M. (2016) ‘Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction’, International Journal of Environmental Science and Technology, 13(6), pp. 1453–1464. Available at: https://doi.org/10.1007/s13762-016-0979-2.

Taiwo, B.O., Gebretsadik, A., Fissha, Y., Kide, Y., Li, E., Haile, K. and Oni, O.A. (2023) ‘Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models’, Journal of Mining and Environment, 14(2), pp. 375–388. Available at: https://doi.org/10.22044/jme.2023.12489.2266.

Tarumasely, N.H., Wardana, N.K. and Prastowo, R. (2024) ‘Analysis of Ground Vibration Levels Due to the Blasting Process at PT . Bumi Suksesindo’, Journal Geocelebes, 8(1), pp. 51–61. Available at: https://doi.org/10.20956/geocelebes.v8i1.32853.

Wang, X., Hosseini, S., Jahed Armaghani, D. and Tonnizam Mohamad, E. (2023) ‘Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting’, Mathematics, 11(10), pp. 1–22. Available at: https://doi.org/10.3390/math11102358.

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Published

2024-04-25