A rock engineering system approach to estimation of blast induced peak particle velocity

Document Type : Research Paper

Author

Department of Mining Engineering, Federal University of Technology, Akure, Nigeria.

10.22059/ijmge.2022.343687.594973

Abstract

This paper presents a novel rock engineering system (RES) based method for estimating blast-induced vibration attenuation risk index and predicting peak particle velocity (PPV). The RES approach involves three key steps, which are the identification of influencing parameters, the construction of an interaction matrix and the rating of parameters based on their influence on ground vibration. The selected parameters are the scale distance (SD), the ratio of the scale distance to stemming divided by the burden (SD/TB), the distance of the monitoring station (D), the scale distance divided by the burden (SD/B), the ratio of the scale distance to powder factor (SD/PF) and the ratio of scale distance to spacing divided by the burden (SD/SB). The results indicated that all the six parameters considered have statistically significant influences on the constructed interaction matrix system, with the SD having the highest weighty factor (21.43%) while SD/TB is the lowest (14.29%). The maximum rating of the parameters is 5, 5, 4, 5, 5, 4 for SD, D, SD/B, SD/PF, SD/SB and SD/TB, respectively. The attenuation risk index ranges from 14.29 to 63.43, and the slope of the actual measured PPV against the calculated attenuation risk index is negative. The developed RES-based model demonstrated better performance and a reliable method for ground vibrations prediction with a higher degree of accuracy, considering its higher determination coefficient (R2 = 0.96) and smaller error (RMSE = 1.08, MAD = 0.79, MAPE = 9.95) compared to multiple regression, Langefors & Kihlstrom and Hudaverdi models.

Keywords

Main Subjects


[1]    Zhang, Z., Hou, D., Guo, Z., He, Z., & Zhang, Q. (2020). Experimental study of surface constraint effect on rock fragmentation by blasting. International Journal of Rock Mechanics and Mining Sciences, 128, 104278. doi: https://doi.org/10.1016/j.ijrmms.2020.104278 
[2]    Adesida, P. A. (2022). Powder factor prediction in blasting operation using rock geo-mechanical properties and geometric parameters. International Journal of Mining and Geo-engineering; 56(1), 25-32. doi: https://doi.org/10.22059/IJMGE.2021.310930.594870
[3]    Rodríguez, R., García de Marina, L., Bascompta, M., & Lombardía, C. (2021). Detsermination of the ground vibration attenuation law from a single blast: A particular case of trench blasting. Journal of Rock Mechanics and Geotechnical Engineering, 13(5), 1182–1192. doi: https://doi.org/10.1016/j.jrmge.2021.03.016
[4]    Lopez-Jimeno, C. L., Jimeno, E., & Carcedo, F. J. A. (1995). Drilling and Blasting of Rocks Rotterdam: A. A. Balkema Publishers.
[5]    Hammed, O. S., Popoola, O. I., Adetoyinbo, A. A., Awoyemi, M. O., Adagunodo, T. A., Olubosede, O., & Bello, A. K. (2018). Peak particle velocity data acquisition for monitoring blast induced earthquakes in quarry sites. Data in Brief, 19, 398-408. doi: https://doi.org/10.1016/j.dib.2018.04.103
[6]    Dumakor-Dupey, N. K., Arya, S., & Jha, A. (2021). Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications. Minerals, 11(6), 601-630. doi: https://doi.org/10.3390/min11060601
[7]    Zhen-xiong, W., Wen-bin, G., Ting, L., Jian-qing, L., Jing-lin, X., & Xin, L. (2016). Blasting Vibration Generated by Breaking-Blasting Large Barriers with EBBLB. Shock and Vibration, 1–13. doi: https://doi.org/10.1155/2016/7503872
[8]    Hudaverdi, T. (2012). Application of multivariate analysis for prediction of blast-induced ground vibrations. Soil Dynamics and Earthquake Engineering; 43, 300-308. doi: http://dx.doi.org/10.1016/j.soildyn.2012.08.002
[9]    Hacefendioglu, K., & Alpaslan, E. (2015). Stochastically simulated blast-induced ground motion effects on non-linear response of an industrial masonry chimney. Stochastic Environmental Research and Risk Assessment, 28 (2) 415–427. https://doi.org/10.1007/s00477-013-0761-7
[10]  Yin, Z., Hu, Z., Wei, Z., Zhao, G., Hai-feng, M., Zhang, Z., & Feng, R. (2018). Assessment of Blasting-Induced Ground Vibration in an Open-Pit Mine under Different Rock Properties. Advances in Civil Engineering, 1–10. doi: https://doi.org/10.1155/2018/4603687  
[11]   Jayasinghe, B., Zhao, Z., Teck Chee, A. G., Zhou, H., & Gui, Y. (2019). Attenuation of rock blasting induced ground vibration in rock-soil interface. Journal of Rock Mechanics and Geotechnical Engineering, 11(4), 770-778. doi: https://doi.org/10.1016/j.jrmge.2018.12.009
[12]  Hacefendioglu, K., & Soyluk, K. (2012). Effects of blast-induced random ground motions on the stochastic behaviour of industrial masonry chimneys. Structural Engineering and Mechanics, 43(6),835–845. doi: https://doi.org/10.12989/
sem.2012.43.6.835
[13]  Hacıefendioglu, K. (2017). Stochastic dynamic response of short-span highway bridges to spatial variation of blasting



ground vibration. Applied Mathematics and Computation, 292, 194–209. https://doi.org/10.1016/j.amc.2016.07.039
[14]  Rehmana, G., khattakb, I., Hamayunc, M., Rahmana, A., Haseeba, M., Umara, M., Alia, S. Iftikhard, W., Shamsa, A., & Pervaiz, R. (2021). Impacts of mining on local fauna of wildlife in District Mardan & District Mohmand Khyber Pakhtunkhwa Pakistan. Brazilian. Journal of Biology, 84(e251733), 1-11. doi: https://doi.org/10.1590/1519-6984.251733
[15]  Gascoyne, M., & Thomas, D. A. (1997). Impact of blasting on groundwater composition in a fracture in Canada’s Underground Research Laboratory. Journal of Geophysical Research; 102(B1), 573-584.
[16]  Adepitan, R. A., Owolabi, A. O., & Komolafe, K. (2018). Prediction of structural response to blast-induced vibration in Kopek Construction Quarry, Ikere-Ekiti, Ekiti State, Nigeria. International Journal of Environmental Studies, 75(6), 990–999. doi: https://doi.org/10.1080/00207233.2018.1473207
[17]  Saadat, M., Khandelwal, M., & Monjezi, M. (2014). An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. Journal of Rock Mechanics and Geotechnical Engineering, 6(1), 67–74. doi: https://doi.org/10.1016/j.jrmge.2013.11.001
[18]  Lawal, A. I., & Idris, M. A. (2019): An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. International Journal of Environmental Studies, 1-17. doi: https://doi.org/10.1080/00207233.2019.
1662186
[19]  Nicholls, H.R., Johnson, C. F., & Duvall, W. I. (1971). Blasting Vibrations and Their Effects on Structures, Bureau of Mines, Washington DC, 1971. Bulletin 656.
[20] Dowding, C. H. (1985). Blast Vibration Monitoring and Control, Prentice–Hall, Englewood Cliffs, NJ.
[21]  Odello, R. J. (1980). Origins and Implications of Underground Explosives Storage Regulations Technical Memorandum, No. 51-80-14, Naval facilities engineering command, USA
[22] Duvall, W. I., & Fogleson, D. E. (1962). Review of criteria for estimating damage to residences from blasting vibration. Report no. 5968 (Washington, DC: United States Bureau of Mines).
[23]  Langefors, U. & Kihlström, B. (1978). The Modern Technique of Rock Blasting. John Wiley & Sons.
[24] Ambraseys, N. R., & Hendron, A. J. (1968). Dynamic behaviour of rock masses. In: K. G. Stagg and O.C. Zeinkiewicz (Eds) Proceedings of the Rock Mechanics in Engineering Practice (London: Wiley), 203–227.
[25]  Indian Standards Institute, (1973) Criteria for safety and design of structures subjected to underground blast. ISI Bulletin IS-6922 (New Delhi: Indian Standards Institute).
[26]  Ragam, P., & Nimaje, D. S. (2018). Evaluation and prediction of blast-induced peak particle velocity using artificial neural network: A case study. Noise & Vibration Worldwide, 49(3), 111–119. doi: https://doi.org/10.1177/0957456518763161
[27]  Dehghani, H., & Ataee-pour, M. (2011). Development of a model to predict peak particle velocity in a blasting operation. International Journal of Rock Mechanics and Mining Sciences, 48(1), 51–58. doi: https://doi.org/10.1016/j.ijrmms.2010.08.005
[28] Nguyen, H., Bui, X., Tran, Q., Le, T., Do, N., & Hoa, L. T. (2019). Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Applied Sciences, 1(1), 125–135. doi: https://doi.org/10.1007/s42452-018-0136-2
[29] Ajaka, E. O., & Adesida, P. A. (2014). Importance of Blast-Design in Reduction of Blast-Induced Vibrations. International Journal of Science, Technology and Society, 2(3), 53-58. doi: https://doi.org/10.11648/j.ijsts.20140203.14
[30]  Jahed, A. D., Hajihassani, M., Mohamad, E. T., Marto, A., & 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), 5383–5396. doi: https://doi.org/10.1007/s12517-013-1174-0
[31]  Skagius, K., Wiborgh M., Ström A., & Morén L. (1997). Performance Assessment of the Geosphere Barrier of a Deep Geological Repository for Spent Fuel: The Use of Interaction Matrices for Identification, Structuring and Ranking of Features, Events and Processes. Nuclear Engineering and Design, 176(1), 155-162.
[32]  Avila, R., & Moberg, L. (1999). A Systematic Approach to the Migration of< Sup> 137 Cs in Forest Ecosystems Using Interaction Matrices. Journal of Environmental Radioactivity, 45(3), 271-282.
[33]  Velasco, H., Ayub, J., Belli, M., & Sansone, U. (2006). Interaction Matrices as a First Step Toward a General Model of Radionuclide Cycling: Application to the 137 Cs Behavior in a Grassland Ecosystem. Journal of Radioanalytical and Nuclear Chemistry, 268(3), 503-509. Wiley). doi: https://doi.org/
10.1007/s10967-006-0198-2
[34]  Agüero, A., Pinedo, P., Simón, I., Cancio, D., Moraleda, M., Trueba, C., & Perez-Sanchez, D.  (2008). Application of the Spanish Methodological Approach for Biosphere Assessment to a Generic High-level Waste Disposal Site. Science of the Total Environment, 403(1), 34-58. doi: https://doi.org/10.1016/
j.scitotenv.2008.04.054
[35]  Mavroulidou, M., Hughes, S. J., & Hellawell, E. E. (2004). A Qualitative Tool Combining an Interaction Matrix and a GIS to Map Vulnerability to Traffic Induced Air Pollution. Journal of Environmental Management, 70(4), 283-289. doi: https://doi.org/10.1016/j.jenvman.2003.12.002
[36]  Condor, J., & Asghari, K. (2009). An Alternative Theoretical Methodology for Monitoring the Risks of CO< sub> 2 Leakage from Wellbores. Energy Procedia, 1(1), 2599-2605.
[37]  Fattahi, H., & Moradi, A. (2017) Risk assessment and estimation of TBM penetration rate using RES-based model. Geotech Geol Eng 35:365–
[38]  Fattahi, H. (2017) Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems. Environ Earth Sci 76:224
[39]  Fattahi, H. (2018). Applying Rock Engineering Systems to Evaluate Shaft Resistance of a Pile Embedded in Rock. Geotechnical and Geological Engineeringhttps://
doi.org/10.1007/s10706-018-0536-5
 
[40] Fattahi, H., & Moradi, A. (2018) A new approach for estimation of the rock mass deformation modulus: a rock engineering systems-based model. Bull Eng Geol Environ 77, 363–374.
[41]  Huang, R., Huang, J., Ju, N., & Li, Y. (2013) Automated tunnel rock classification using rock engineering systems. Eng Geol. 156, 20–27
[42] Fattahi, H. (2018) An estimation of required rotational torque to operate horizontal directional drilling using rock engineering systems. J Pet Sci Technol 8:82–96.
[43]  Faramarzi, F, Mansouri, H., & Ebrahimi-Farsangi, M. A. (2014). Development of rock engineering systems-based models for fly rock risk analysis and prediction of flyrock distance in surface blasting. Rock Mechanics and Rock Engineering, 47, 1291–1306. doi: https://doi.org/10.1007/s00603-013-0460-1
[44] Saffari, A., Sereshki, F., Ataei, M. & Ghanbari, K. (2013). Applying Rock Engineering Systems (RES) approach to Evaluate and Classify the Coal Spontaneous Combustion Potential in Eastern Alborz Coal Mines. Int. J. Min.& Geo-Eng., 47(2), 115-127. https://doi.org/10.22059/ijmge.2013.51333
[45]  Hudson, J. A. (1992). Rock engineering systems: theory and practice. Ellis Horwood, Chichester.
[46]  Mazzoccola, D. F., & Hudson, J. A. (1996). A Comprehensive Method of Rock Mass Characterization for Indicating Natural Slope Stability. Quarterly Journal of Engineering Geology; 29, 37 – 56. doi: http://qjegh.lyellcollection.org/
[47]  Yang, Y., & Zhang, Q. (1998). The application of neural networks to rock engineering systems (RES). International Journal of Rock Mechanics and Mining Science, 35, 727–745. doi: doi: https://doi.org/10.1016/S0148-9062(97)00339-2
[48] Zare-Naghadehi, M., Jimenez, R., KhaloKakaie, R., & Jalali, S. M. E. (2011). A probabilistic systems methodology to analyze the importance of factors affecting the stability of rock slopes. Eng. Geol.; 118, 82–92. doi: https://10.1016/j.enneo.2011.01.003
[49] Zare-Naghadehi, M., Jimenez, R., KhaloKakaie, R., & Jalali, S. M. E. (2013). A new open-pit mine slope instability index defined using the improved rock engineering systems approach. International Journal of Rock Mechanics and Mining Science, 61, 1–14.  doi: http://dx.doi.org/10.1016/j.ijrmms.2013.01.012
[50]  Frough, O., & Torabi, S. R. (2013). An application of rock engineering systems for estimating TBM downtimes. Engineering Geology, 157, 112–123. doi: https://dx.doi.org/10.1016/j.enggeo.2013.02.003
[51]  Faramarzi, F., Ebrahimi Farsangi, M. A. & Mansouri, H. (2013). An RES based model for risk assessment and prediction of back break in bench blasting. Rock Mech Rock Eng.; 46, pp.877–887. https://dx.doi.org/10.1016/j.ijrmms.2012.12.045
[52]  Hudson, J. A. (2013). Review of Rock Engineering Systems applications over the last 20 years. In Rock Characterisation, Modelling and Engineering Design Methods. Taylor & Francis Group: London, UK, 419–424. doi: https://dx.doi.org/10.1201/b14917-75
[53]  Jiao, Y., & Hudson, J. A. (1998). Identifying the critical mechanism for rock engineering design. Géotechnique, 48, 319–335.
[54]  Benardos, A. G., & Kaliampakos, D. C. (2004): A Methodology for Assessing Geotechnical Hazards for TBM Tunnelling—Illustrated by the Athens Metro, Greece. International Journal of Rock Mechanics and Mining Sciences, 4, 987–999. doi: https://doi.10.1016/j.ijrmms.2004.03.007
[55]  Mohammadi. M., & Azad, A. (2020). Applying Rock Engineering Systems Approach for Prediction of Overbreak Produced in Tunnels Driven in Hard Rock. Geotechnical and Geological Engineering, 38, 2447–2463. doi: https://doi.org/10.1007/s10706-019-01161-z
[56]  Singh, P. K., Roy, M. P., Paswan, R. K., Sarim, Md., Kumar, S., & Jha, R. R. (2015). Rock fragmentation control in opencast blasting. Journal of Rock Mechanics and Geotechnical Engineering, 8(2), 225-237. doi: https://doi.org/
10.1016/j.jrmge.2015.10.005
[57]  Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A., & 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), 5383–5396. doi: https://doi.org/
10.1007/s12517-013-1174-0
[58]  Konya, C. J., & Walter, E. J. (1990). Surface blast design. New Jersey: Prentice Hall.
[59]  Salmi, E. F., & Sellers, E. J. (2021). A review of the methods to incorporate the geological and geotechnical characteristics of rock masses in blastability assessments for selective blast design. Engineering Geology, 281, 105970. doi: https://doi.org/10.1016/j.enggeo.2020.105970
[60]  Cunningham, C. V. B. (1983). The Kuz-Ram model for prediction of fragmentation from blasting. In: Proceedings of the first international symposium on rock fragmentation by blasting. Lulea, Sweden; 23–26 August 1983. p. 439-453.
[61]  Hasanipanah, M., Armaghani, D. J., Monjezi, M., & Shams, S. (2016). Risk Assessment and Prediction of Rock Fragmentation Produced by Blasting Operation: A Rock Engineering System. Environmental Earth Sciences, 75, 1–12. doi: https://doi.10.1007/s12665-016-5503-y