Predicting the peak particle velocity from rock blasting operations using Bayesian approach

• Autorzy: Aladejare Adeyemi Emman , Lawal Abiodun Ismail , Onifade Moshood

Identyfikatory

DOI

10.1007/s11600-022-00727-5

• Języki publikacji: EN

Abstrakty

EN

Measuring the blast-induced ground vibration at blasting sites is very important, to plan and avoid adverse effects of blasting in terms of the peak particle velocity (PPV). However, the measurement of PPV often requires time, cost, and logistic commitment, which may not be economical for small-scale mining operations. This has prompted the development of numerous regression equations in the literature to estimate PPV from a relatively easier to estimate scaled distance (SD) measurement. With numerous regression equations available in the literature, there is a challenge of how to select the appropriate model for a specific blasting site, more so that rocks behave differently from site to site because of different geological processes that rocks are subjected to. This study develops a method that selects appropriate models for specific blasting sites by comparing the evidence and occurrence probability of different regression models. The appropriate model is the model with the highest evidence and occurrence probability given the available blasting site SD data. The selected model is then integrated with prior knowledge and available blasting SD data in Bayesian framework for probabilistic characterization of PPV. The SD and PPV data at the opencast coal mine, Jharia coalfield in the Dhanbad district of Jharkhand, India, is used to illustrate and validate the approach. The mean and standard deviation of simulated PPV samples from the proposed approach are 12.38 mm/s and 7.36 mm/s, respectively, which are close to the mean of 12.03 mm/s and standard deviation of 9.24 mm/s estimated from the measured PPV at the site. In addition, the probability distribution of the simulated PPV samples is consistent with the probability distribution of the measured PPV at the blasting site.

Słowa kluczowe

EN

Bayesian method; model selection; probability distribution; peak particle velocity; regression model; rock blasting

PL

metoda bayesowska; wybór modelu; rozkład prawdopodobieństwa; maksymalna prędkość cząstek; model regresji; wysadzanie skał

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 2
• Strony: 581--591
• Opis fizyczny: Bibliogr. 38 poz.

Twórcy

autor

Aladejare Adeyemi Emman

• Oulu Mining School, University of Oulu, Oulu, Finland

• https://orcid.org/0000-0002-0599-3799

autor

Lawal Abiodun Ismail

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

autor

Onifade Moshood

• Department of Civil and Mining Engineering, University of Namibia, Windhoek, Namibia

Bibliografia

• 1. Abdel-Rasoul EI (2000) Assessment of the particle velocity characteristics of blasting vibrations at Bani Khalid quarries. Bull Fac Eng 28(2):135–150
• 2. Ak H, Iphar M, Yavuz M, Konuk A (2009) Evaluation of ground vibration effect of blasting operations in a magnesite mine. Soil Dyn Earthq Eng 29(4):669–676
• 3. Akande JM, Aladejare EA, Lawal AI (2014) Evaluation of the environmental impacts of blasting in Okorusu Fluorspar Mine Namibia. Int J Eng Technol 4(1):35–40
• 4. Aladejare AE, Idris MA (2020) Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods. J Rock Mech Geotech Engin 12(6):1263–1271
• 5. Aladejare AE, Wang Y (2017) Sources of uncertainty in site characterization and their impact on geotechnical reliability-based design. ASCE-ASME J Risk Uncertain Eng Syst, Part A: Civil Eng 3(4):04017024
• 6. Aladejare AE, Wang Y (2018) Influence of rock property correlation on reliability analysis of rock slope stability: from property characterization to reliability analysis. Geosci Front 9(6):1639–1648
• 7. Aladejare AE, Wang Y (2019a) Probabilistic characterization of Hoek-Brown constant m i of rock using Hoek’s guideline chart, regression model and uniaxial compression test. Geotech Geol Eng 37(6):5045–5060
• 8. Aladejare AE, Wang Y (2019b) Estimation of rock mass deformation modulus using indirect information from multiple sources. Tunn Undergr Space Technol 85:76–83
• 9. Aladejare AE, Akeju VO, Wang Y (2020) Probabilistic characterisation of uniaxial compressive strength of rock using test results from multiple types of punch tests. Georisk Assess Manage Risk Eng Syst Geohazards 2020:1–2
• 10. Aladejare AE, Akeju VO, Wang Y (2022) Data-driven characterization of the correlation between uniaxial compressive strength and Youngs’ modulus of rock without regression models. Transp Geotech 32:100680
• 11. Aloui M, Bleuzen Y, Essefi E, Abbes C (2016) Ground vibrations and air blast effects induced by blasting in open pit mines: case of Metlaoui mining Basin, South western Tunisia. J Geol Geophys. https://doi.org/10.4172/2381-8719.1000247
• 12. Ambraseys NR, Hendron AJ (1968) Dynamic behavior of rock masses. In: Stagg K, Wiley J (eds) Rock mechanics in engineering practice. Wiley, London, pp 203–207
• 13. Ang AH, Tang WH (2007) Probability concepts in engineering planning and design: emphasis on application to civil and environmental engineering. Wiley
• 14. Badal K (2010) Blast vibration studies in surface mines. PhD Thesis. India; National Institute of Technology Rourkela
• 15. Duvall WI, Petkof B (1959) Spherical propagation of explosion generated strain pulses in rock. U.S. Department of the Interior, Bureau of Mines.
• 16. Ghosh A, Daemen JJ (1983) A simple new blast vibration predictor (based on wave propagation laws). In: The 24th US symposium on rock mechanics (USRMS). OnePetro
• 17. Indian Standard (IS) (1973) Criteria for safety and design of structures subjected to underground blast. Bulletin No: IS-6922, Bureau of Indian Standards, New Delhi, India
• 18. Kahriman A (2002) Analysis of ground vibrations caused by bench blasting at can open-pit lignite mine in Turkey. Environ Geol 41(6):653–661
• 19. Kahriman A (2004) Analysis of parameters of ground vibration produced from bench blasting at a limestone quarry. Soil Dyn Earthq Eng 24(11):887–892
• 20. Kahriman A, Ozer U, Aksoy M, Karadogan A, Tuncer G (2006) Environmental impacts of bench blasting at Hisarcik boron open pit mine in Turkey. Environ Geol 50(7):1015–1023
• 21. Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J R Mech M Sci 46(7):1214–1222
• 22. Kumar S, Mishra AK, Choudhary BS, Sinha RK, Deepak D, Agrawal H (2020) Prediction of ground vibration induced due to single hole blast using explicit dynamics. Min Metall Explor 37:1–9
• 23. Langefors U, Kihlstrom B (1963) The modern technique of rock blasting. Wiley, New York
• 24. Lawal AI (2020) An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria. Sci Afr, Sci Afr 8:e00413
• 25. Lawal AI (2021) A new modification to the Kuz-Ram model using the fragment size predicted by image analysis. Int J Rock Mech Min Sci 138:104595. https://doi.org/10.1016/j.ijrmms.2020.104595
• 26. Lawal AI, Kwon S (2020) Application of artificial intelligence in rock mechanics: an overview. J Rock Mech Geotech Eng. https://doi.org/10.1016/j.jrmge.2020.05.010
• 27. Lawal AI, Olajuyi SI, Kwon S, Aladejare AE, Edo TM (2021) Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm. Acta Geophys: 1–2.
• 28. Mesec J, Kovač I, Soldo B (2010) Estimation of particle velocity based on blast event measurements at different rock units. Soil Dyn Earthq Eng 30(10):1004–1009
• 29. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092
• 30. Nicholson RF (2005) Determination of blast vibrations using peak particle velocity at Bengal Quarry, in St Ann, Jamaica
• 31. Ozer U (2008) Environmental impacts of ground vibration induced by blasting at different rock units on the Kadikoy-Kartal metro tunnel. Eng Geol 100(1–2):82–90
• 32. Pal Roy P (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Min Sci Technol 12(2):157–165
• 33. Rai R, Singh TN (2004) A new predictor for ground vibration prediction and its comparison with other predictors
• 34. Siskind DE, Stagg MS, Kopp JW, Dowding CH (1980) Structure response and damage produced by ground vibrations from surface blasting, RI 8507. US Bureau of Mines, Washington
• 35. Wang Y, Aladejare AE (2015) Selection of site-specific regression model for characterization of uniaxial compressive strength of rock. Int J Rock Mech Min Sci 75:73–81
• 36. Wang Y, Aladejare AE (2016a) Bayesian characterization of correlation between uniaxial compressive strength and Young’s modulus of rock. Int J Rock Mech Min Sci 85:10–19
• 37. Wang Y, Aladejare AE (2016b) Evaluating variability and uncertainty of geological strength index at a specific site. Rock Mech Rock Eng 49(9):3559–3573
• 38. Wang Y, Cao Z (2013) Probabilistic characterization of Young’s modulus of soil using equivalent samples. Eng Geol 159:106–118

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).