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Year 2025
 
Vol 66, Issue 2, [04 - 2025] - Full papers

Prediction of flyrock distance in open-pit mines using an optimized artificial neural network with evolution strategies

DOI:10.46326/JMES.2025.66(2).03

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Hanoi, Vietnam
    3 Vietnam Mining Science and Technology Association, Hanoi, Vietnam
    4 Vinacomin - Minerals Holding Corporation, Hanoi, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 14th-Nov-2024
  • Revised: 18th-Feb-2025
  • Accepted: 28th-Feb-2025
  • Online: 1st-Apr-2025
Pages: 15 - 28
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Abstract:

Blasting is a fundamental technique in open-pit mining, used to break rock and ore. Its effectiveness and the degree of fragmentation significantly affect the efficiency of subsequent processes and the overall mine productivity. However, a major concern is the dangerous impact of flyrock, which poses serious safety risks to personnel and equipment in the vicinity, potentially leading to fatal accidents. This paper presents an advanced machine learning model, named ES-ANN, which combines an Artificial Neural Network (ANN) with Evolution Strategies (ES) to predict flyrock distance in open-pit mines with high accuracy. The ANN model is used to forecast flyrock distances, while the ES technique optimizes the model's weights, enhancing prediction accuracy. To evaluate the improvement of the proposed ES-ANN model, another optimization model based on the Evolutionary Programming (EP) optimization algorithm and ANN (abbreviated as EP-ANN), and a standalone ANN model were developed and compared based on the same datasets. Blasting data from the Ta Phoi copper mine (Lao Cai) was utilized for model training and validation. The results indicated that the ES-ANN model achieved the highest performance with an MAE of 2.095, RMSE of 2.711, and R2 of 0.952 on the testing dataset (95.2% accuracy) in predicting flyrock distance. Meanwhile, the EP-ANN and standalone ANN models only provided MAE of 5.512 and 7.300, RMSE of 6.692 and 8.938, and R2 of 0.708 and 0.479, respectively. Compared to the EP and traditional methods, the ES-ANN model offered superior accuracy and reliability, making it an effective tool for forecasting and managing flyrock hazards in open-pit mining, thus enhancing operational safety.

How to Cite
., H.Nguyen, ., B.Dinh Tran, ., N.Xuan Bui, ., A.Dinh Nguyen, ., V.Van Pham, ., H.Thu Thi Le, ., T.Qui Le, ., H.Ngoc Do, Le, N.Tuan and Nguyen, T.Tuan 2025. Prediction of flyrock distance in open-pit mines using an optimized artificial neural network with evolution strategies. Journal of Mining and Earth Sciences. 66, 2 (Apr, 2025), 15-28. DOI:https://doi.org/10.46326/JMES.2025.66(2).03.
References

Armaghani, D. J., Mahdiyar, A., Hasanipanah, M., Faradonbeh, R. S., Khandelwal, M., and Amnieh, H. B. (2016). Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting. Rock Mechanics and Rock Engineering, 49(9), 3631-3641, doi:10.1007/s00603-016-1015-z.

Armstrong, L. W. (1994). The quality of stemming in assessing blasting efficiency. UNSW Sydney,

Bach, N. V., Quyen, L. V., Ninh, L. N., and An, N. D. (2015). Modern Blasting Technology with Large-Diameter Boreholes Applied to Construction Material Quarries in Vietnam.  Hanoi. Natural Science and Technology (in Vietnamese).

Back, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, Genetic Algorithms. Oxford University Press.

Beyer, H. G., and Arnold, D. V. (2001). Theory of evolution strategies-A tutorial: Springer.

Chen, Y., Wang, M., Yin, H., and Zhang, T. (2024). Prediction of flyrock distance induced by blasting using particle swarm optimization and multiple regression analysis: an engineering perspective. Acta Geophysica, 72(1), 287-301.

Desai, M., and Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth, 4, 1-11.

Dong, L. (2024). Residents are frustrated as companies' blasting operations for stone extraction cause environmental pollution (in Vietnamese). https://laodong.vn/photo/nguoi-dan-buc-xuc-vi-doanh-nghiep-no-min-khai-thac-da-gay-o-nhiem-moi-truong-1355229.ldo. Accessed 04/09/2024.

Fogel, L. (1964). On the organization of intellect (Ph. D. thesis). University of California, Los Angeles, CA, USA.

Fogel, L. J. (1962). Autonomous automata. Industrial research, 4, 14-19.

Ghasemi, E., Sari, M., and Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences, 52, 163-170.

Hansen, N., Arnold, D. V., and Auger, A. (2015). Evolution strategies. Springer handbook of computational intelligence. 871-898.

Jahed Armaghani, D., Tonnizam Mohamad, E., Hajihassani, M., Alavi Nezhad Khalil Abad, S., Marto, A., and Moghaddam, M. (2016). Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Engineering with Computers, 32, 109-121.

Khashei, M., Hamadani, A. Z., and Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39(3), 2606-2620.

Law, P. O. T. (2024). Company's Blasting Operation Sends Rocks Flying into Homes: All Mining Activities Temporarily Suspended (In Vietnamese). https://baovephapluat.vn/kinh-te/do-thi-xay-dung/vu-doanh-nghiep-no-min-lam-da-vang-vao-nha-dan-tam-dung-moi-hoat-dong-khai-thac-mo-155572.html. Accessed 04/09/2024.

Li, C., Zhou, J., Du, K., Armaghani, D. J., and Huang, S. (2023). Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression. Natural Resources Research, 32(6), 2995-3023, doi:10.1007/s11053-023-10259-4.

Liddell, K. (2021). Investigation into methods to identify and accurately locate misfired explosive charges following drill and blast operations. University of Southampton,

Lu, X., Hasanipanah, M., Brindhadevi, K., Bakhshandeh Amnieh, H., and Khalafi, S. (2020). ORELM: a novel machine learning approach for prediction of flyrock in mine blasting. Natural Resources Research, 29, 641-654.

Luat, B. V. P. (2024). The quarry blasting incident that sent rocks into residents' homes: Compensation has been provided (in Vietnamese). https://baovephapluat.vn/van-hoa-xa-hoi/doi-song-xa-hoi/vu-mo-da-no-min-lam-da-vang-vao-nha-dan-da-thuc-hien-den-bu-thiet-hai-155920.html. Accessed 04/09/2024.

Naskath, J., Sivakamasundari, G., and Begum, A. A. S. (2023). A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN. Wireless Personal Communications, 128(4), 2913-2936.

Nayak, N. P., Jain, A., and Mahapatra, S. R. (2022). Application of mine excellence software in flyrock prediction and mitigation. Materials Today: Proceedings, 48, 1271-1276.

Nien, T. (2024). The 'nightmare' of residents living near quarry blasting sites (in Vietnamese). https://thanhnien.vn/con-ac-mong-cua-nhung-ho-dan-song-gan-khu-no-min-khai-thac-da-185240706121712204.htm. Accessed 04/09/2024.

Phong, T. (2024). Unannounced quarry blasting leaves dozens of households in fear (in Vietnamese). https://tienphong.vn/no-min-khai-thac-da-khong-thong-bao-khien-hang-chuc-ho-dan-khiep-via-post1621886.tpo. Accessed 04/09/2024.

Plus, E. (2024). Thanh Hoa: Rocks Fly into Residential Homes During Quarry Blasting (In Vietnamese). https://moitruongplus.com.vn/11/17743/Thanh-Hoa-Da-bay-vao-nha-dan-khi-no-min-khai-thac-mo-da.htm. Accessed 04/09/2024.

Purba, R. D. (1992). Control of blasting in surface mining with reference to flyrock. UNSW Sydney,

Raina, A., and Murthy, V. (2016). Prediction of flyrock distance in open pit blasting using surface response analysis. Geotechnical and Geological Engineering, 34, 15-28.

Raina, A. K., and Bhatawdekar, R. M. (2022). Blast-induced flyrock: risk evaluation and management. In Risk, reliability and sustainable remediation in the field of civil and environmental engineering (pp. 209-247): Elsevier.

Rudolph, G. (2000). Evolution strategies. Evolutionary computation, 1, 81-88.

Sawmliana, C., Hembram, P., Singh, R. K., Banerjee, S., Singh, P., and Roy, P. P. (2020). An investigation to assess the cause of accident due to flyrock in an opencast coal mine: a case study. Journal of The Institution of Engineers (India): Series D, 101, 15-26.

Thang, D. T., Nam, B. X., and Hieu, T. Q. (2015). Blasting in mining and construction industries. Hanoi.Science and Technics Publishing House.

Trade, V. M. O. I. A. (2019). QCVN 01:2019/BCT - National Technical Regulation on Safety in the Production, Testing, Acceptance, Storage, Transportation, Use, Disposal of Industrial Explosives, and the Storage of Explosive Precursors (In Vietnamese).

Uncuoglu, E., Citakoglu, H., Latifoglu, L., Bayram, S., Laman, M., Ilkentapar, M., et al. (2022). Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems. Applied Soft Computing, 129, 109623.

Van der Walt, J., and Spiteri, W. (2020). A critical analysis of recent research into the prediction of flyrock and related issues resulting from surface blasting activities. Journal of the Southern African Institute of Mining and Metallurgy, 120(12), 701-714.

Xu, Y., Li, F., and Asgari, A. (2022). Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms. Energy, 240, 122692.

Zhang, J., Li, C., Yin, Y., Zhang, J., and Grzegorzek, M. (2023). Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer. Artificial Intelligence Review, 56(2), 1013-1070.

Zhang, R., Li, Y., Gui, Y., Armaghani, D. J., and Yari, M. (2024). A stacked multiple kernel support vector machine for blast induced flyrock prediction. Geohazard Mechanics, 2(1), 37-48, doi:https://doi.org/10.1016/j.ghm.2024.01.002.

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