Use of RocFall Software for Flyrock Simulation in Open-Pit Mining

Authors

  • D IBARRA-GONZÁLEZ Author
  • M. CÁNOVAS Author
  • E. MARTÍNEZ-BAUTISTA Author
  • J. ARZÚA Author
  • E. TRIGUEROS Author

DOI:

https://doi.org/10.29227/IM-2026-01-01-023

Keywords:

flyrock, blasting, open-pit mining, RocFall

Abstract

In open-pit mining, a flyrock corresponds to the projection of a fragment because of the energy released in a blast, which increases the risk to personnel, equipment, and infrastructure. In recent decades, several models have been generated that attempt to predict the velocity and maximum projection distance to define safety zones. However, there is no generalized vision on how to approach this phenomenon, nor is there a widespread approach to its analysis using software. Under the assumption that an analogy can be assumed between the flyrock phenomenon and the rockfall considering an initial acceleration, the use of RocFall software is proposed to simulate this phenomenon. For this purpose, a case study was generated considering a conventional blasting with 10-meter high benches and an initial velocity based on existing empirical models. The three scenarios studied were: (i) a base case without considering the restitution coefficient, (ii) a case considering the restitution coefficient, and (iii) a three-dimensional model. A total of 100 possible trajectories were simulated, measuring the range of each and the distribution of impacts along the study profile. The importance of considering the coefficient of restitution in the analysis is evident because many existing models only consider the distance reached by the initial impact of the fragment and not what happens to the fragment after the impact. Finally, it was concluded that RocFall can be used as a visual tool for the study of projections, and different lines for future applications are proposed.

Author Biographies

  • D IBARRA-GONZÁLEZ

    Metallurgical and Mining Engineering Department, Universidad Católica del Norte, Chile; email: daniel.ibarra@ucn.cl

  • M. CÁNOVAS

    Metallurgical and Mining Engineering Department, Universidad Católica del Norte, Chile

  • E. MARTÍNEZ-BAUTISTA

    Metallurgical and Mining Engineering Department, Universidad Católica del Norte, Chile

  • J. ARZÚA

    Metallurgical and Mining Engineering Department, Universidad Católica del Norte, Chile

  • E. TRIGUEROS

    Mining and Civil Engineering Department, Universidad Politécnica de Cartagena, Spain

References

Ebrahimi, E., Monjezi , M., Khalesi, M. R., & Armaghani, D. J., Prediction and optimization of back - break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bull. Eng. Geol. Environ., 2016, vol. 75, no. 1, pp. 27 – 36.

Hödaverdi, T., & Akyildiz, O., Investigation of blast fragmentation models in a sandstone quarry. Sci. Min. J., 2020, vol. 59, no. 3, pp. 145 – 156.

Eze, C. L. & Usani, U. U., Hard Rock Quarry Seismicity and Face Bursting Flyrock Range Prediction in the Granite and Migmatites Rocks of North Central Nigeria. Int. J. Eng. Res. App., 2014, vol. 4, no, 12, pp. 1 – 6.

Kricak, L., Kecojevic, V., Negovanovic, M., Jankovic, I., & Zekovic, D., Environmental and safety accidents related to blasting operation. Am. J. Environ. Sci., 2012, vol. 8, no. 4, pp. 360 – 365

Akande, J. M., Aladejare, A. & Abiodun, I. L., Evaluation of the Environmental Impacts of Blasting in Okorusu Fluorspar Mine, Namibia. Int. J. Eng. Technol., 2014, vol. 4, no. 2, pp. 101 – 108.

Kecojevic, V. & Radomsky, M, Flyrock phenomena and area security in blasting - related accidents. Safety Science, 2005, vol. 43, no. 9, pp. 739 – 750.

An, H. M., Liu, H. Y., Han, H., Zheng, X., & Wang, X. G., Hybrid finite - discrete element modelling of dynamic fracture and resultant fragment casting and muck - piling by rock blast. Comput. Geotech., 2017, vol. 81, pp. 322 – 345.

Ouchterlony, F., & Sanchidrián , J. A., A review of development of better prediction equations for blast fragmentation. J. Rock Mech. Geotch. Eng., 2019, vol. 11, no. 5, pp. 1094 – 1109.

López Jimeno, C., López Jimeno, E., & Bermúdez, P., Manual de Perforación, Explosivos y Voladuras: Minería y Obras públicas. Grupo Proyectos de Ingeniería, Madrid, España, 2017.

Hosseini, S., Poormirzaee, R., Hajihassani, M., & Kalatehjari, R., An ANN‑Fuzzy Cognitive Map‑Based Z‑Number Theory to Predict Flyrock Induced by Blasting in Open‑Pit Mines, Rock Mech, Rock Eng, 2022, vol. 55, pp. 4373 - 4390

Singh, T.N., & Singh, V., An intelligent approach to prediction and control ground vibration in mines. Geotech. Geol. Eng., 2005, vol. 23, pp. 249 - 262.

Sanchidrián, J.A., Segarra, P., & López, L.M., Energy components in rock blasting. Int. J. Rock Mech. Min. Sci., 2007, vol. 44, pp.130 - 147.

Raina, A. K., Murthy, V.M., & Soni, A.K., Flyrock in bench blasting: a comprehensive review. Bull. Eng. Geol. Environ., 2014, vol. 73, pp. 119 - 120.

Szendrei T., & Tose, S., Flyrock in Surface mining Part II - Causes, sourse, and mechanism of rock projection. J. South. Afr. Inst. Min. Metall., 2023, vol. 123, no. 12, pp.557 - 564.

Szendrei T., & Tose, S., Flyrock in surface mining - Limitations of current predictive models and better alternative through modelling the aerodynamics of flyrock trajectory J. South. Afr. Inst. Min. Metall, 2022, vol. 122, no. 12, pp. 725 – 732.

Mishra, R., Mishra, A.K., & Choudhary, B.S., High - Speed Motion Analysis - Based Machine Learning Models for Prediction and Simulation of Flyrock in Surface Mines. App. Sci., 2023, vol. 13, no. 17, pp. 9906.

Raina, A. K., Murthy, V.M., & Soni, A.K., Flyrock in surface mine blasting: understanding the basics to develop a predictive regime. Curr. Sci., 2015, vol. 108, no. 4, pp. 660 - 665

Roy, P.P., Rock Blasting: Effects and Operations; CRC Press: Boca Raton, FL, USA, 2005.

Moore, J.A., & Richrads A.B. Kalgoorlie consolidates gold mines: golden pieke cut - back flyrock control and calibration of a predictive model. Terrock Consulting Engineers, 2005.

Ghasemi, E., Sari, M., & Ataei, M., Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surfaces mines. Int. J. Rock. Mech. Min. Sci., 2012, vol. 52, pp. 163 – 170

Armaghani, D.J., Mahiyar, A., Hasanipanah, M, Faradonbeh, R.S., Khandelwal, M., & Amnich, H.B., Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting. Rock Mech. Rock Eng., 2016, vol. 49, pp. 3631 - 3641.

Supandi S., New formula to determine flyrock distance on sedimentary rocks with low strength. Open Geosci., 2024, vol. 16, no. 1.

Zhou, J., Zhang, Y. & Qiu, Y., State - of - the - art review of machine learning and optimization algorithms applications in environmental effects of blasting. Artif. Intell. Rev., 2024, vol. 57, no. 5.

Lundborg, N., Persson, A., Ladegaard - Pedersen, A., & Holmberg, R., Keeping the lid on flyrock in openpit blasting. Eng. Min. J., 1975, vol. 176, pp. 95 - 100.

Chiapetta, R.F., Application of high - speed photography in testing conventional and “Hand - Made” surfave delays. Square knots and blast analysis (Bsc Thesis). Mining Engineering Department. Universidad de Queen, Kingston, Ontario, Canadá, 1980.

Gupta, R.N., Surface Blasting and its impact on environment. Impact of mining on environment,1980, pp. 23 - 24

Roth, J., A model for the determination of flyrock range as a function of shot condition. United States Department of the Interior Bureau of Mines, 1979.

Murlidhar, B.R., Nguyen, H., Rostami, J., Bui, X., Armaghani, D.J., Ragam, P., & Mohamad, E.T., Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization - based multi - layer perceptron neural network. J. Rock Mech. Geotech. Eng., 2021, vol. 13, pp. 1413 - 1427.

Lundborg, N., The hazard of flyrock in rock blasting. Swedish Rock Blasting Committee, Stockholm, Sweden., 1974.

Mckenzie, C.K., Flyrock range & Fragment Size prediction. 35th ISEE Annual Conference, Denver, CO, USA, 2009.

Stojadinovic, S., Pantovic, R., & Zikic, M., Prediction of flyrock trajectories for forensic applications using ballistic flight equations. Int. J. Rock Mech. Min. Sci., 2011, vol. 48, pp. 1086 - 1094.

Cánovas, M., Reyes, K., Arzúa, J., Tapia, E. & Meza, R., Desarrollo de un modelo de predicción de flyrocks para minería a cielo abieto. Proceeding Primer Congreso Chileno de Mecánica de Rocas 2023, 2023, pp. 495 - 506.

Shakeri, J., Amini Khoshalan, H., Dehghai, H., Bascompta, M., & Onyelowe, K., Developing new model for flyrock distance assessment in open - pit mines. J. Min. Environ., 2022, vol. 13, no. 2, pp. 375 - 389.

Babaeian, M., Sereshki, F.m Ataei, M., Nehring, M., & Mohammadi, S. Application of Soft Computing, Statistical and Multi - Criteria Decision - Making Methods to develop a predictive equation for prediction of flyrock distance in open - pit mining. Mining, 2023, vol. 3, pp. 304 - 333

Zhang, R., Li, Y., Gui, Y., Armaghani, D.J., & Yari, M., A stacked deep multi - kernel learning framework for blast induced flyrock prediction. Int. J. Rock Mech. Min. Sci., 2024, vol. 177, 105741

Van der Walt, J., & Spiteri, W., A critical analysis of recent research into the prediction of flyrock and related issues resulting from surface blasting activities. J. South Afr. Inst. Min. Metall., 2020, vol. 120, no.12, pp 701 - 714

Monjezi, M., Bahrami, A., & Yazdian Varjani, A., Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int. J. Rock Mech. Min. Sci., 2010, vol. 47, no. 3, pp. 476 – 480.

Monjezi, M., Bahrami, A., Varjani, A. Y., & Sayadi, A. R., Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab. J. Geosci., 2011, vol. 4, no. 3 – 4, pp. 421 – 425.

Al - Bakri, A. Y., & Sazid, M. Application of Artificial Neural Network (ANN) for Prediction and Optimization of Blast - Induced Impacts. Mining, 2021, vol. 1, no. 3, pp. 315 – 334.

Krop, I., Takahashi, Y., Sasaoka, T., Shimada, H., Hamanaka, A., & Onyango, J. Assessment of Selected Machine Learning Models for Intelligent Classification of Flyrock Hazard in an Open Pit Mine. IEEE Access, 2024, vol. 12, pp. 8585 – 8608.

Amini, H., Gholami, R., Monjezi, M., Torabi, S. R., & Zadhesh, J., Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Computing and Applications, 2012, vol. 21, no. 8, pp. 2077 – 2085

Rad, H. N., Hasanipanah, M., Rezaei, M., & Eghlim , A. L., Developing a least squares support vector machine for estimating the blast - induced flyrock. Eng, Comput., 2018, vol. 34, no. 4, pp. 709 – 717.

Faradonbeh, R. S., Jahed Armaghani, D., & Monjezi, M., Development of a new model for predicting flyrock distance in quarry blasting: a genetic programming technique. Bull. Eng. Geol. Environ., 2016, vol. 75, no. 3, pp. 993 – 1006.

Van der Walt, J., & Spiteri, W., A conceptual technique to mathematically quantify trajectory of flyrocks . J. South. Afr. Inst. Min. Metall., 2023, vol. 123, no. 4., pp. 165 - 174.

Morken, E., Performance of Three Rockfall Models Tested at Historic Rockfall Sites in Western Norway [Master’s Thesis, University of Oslo]. University of Oslo, Oslo, Norway, 2021.