Document Type : Original Research Paper
Authors
Department of Mining Engineering, Istanbul Technical University, Istanbul, Türkiye
10.22044/jme.2025.15686.3016
Abstract
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration is defined peak particle velocity (ppv) in the ground. All data used to estimation were obtained by observing real blasting operations. After the measuring of the peak particle velocity, models of the prediction were created using independent site parameters. Most of the data is used to train the model, while remaining part is used for testing. The models were created using independent blasting parameters proportionally. Thus, more parameters are included in the models without complicating the models. A thorough validation process was conducted utilizing a diverse set of nine error criteria. Artificial intelligence models have been found to outperform traditional methods in predicting ground vibration. The mean absolute error values were found to be 1.42, 1.54, and 1.78 for ANFIS, GPR, and SVM, respectively. A similar situation is observed for other error criteria as well. ANFIS appears to be the most effective model for predicting ground vibration.
Keywords
- Adaptive-network-based fuzzy inference system (ANFIS)
- Blasting
- Gaussian process regression (GPR)
- Support vector machine (SVM)
Main Subjects
[1]. Jimeno, C. L., Jimeno, E. L., Carcedo, F. J. A., & de Ramiro, Y. V. (1995). Drilling änd blasting of rocks. USA CRS Press, 41, 35947.
[2]. Konya, C. J., & Walter, E. J. (1990). Surface blast design. (No Title).
[3]. Mansouri, H., & EBRAHIMI, F. M. (2015). Blast vibration modeling using linear superposition method.
[4]. Kahriman, A. (2004). Analysis of parameters of ground vibration produced from bench blasting at a limestone quarry. Soil Dynamics and Earthquake Engineering, 24(11), 887-892.
[5]. Ataei, M. (2010). Evaluation of blast induced ground vibrations from underground excavation at Karoun 3 area. Mining Technology, 119(1), 7-13.
[6]. Yilmaz, O. (2016). The comparison of most widely used ground vibration predictor equations and suggestions for the new attenuation formulas. Environmental earth sciences, 75(3), 269.
[7]. Hudaverdi, T., & Akyildiz, O. (2019). Evaluation of capability of blast-induced ground vibration predictors considering measurement distance and different error measures. Environmental Earth Sciences, 78(14), 421.
[8]. Soltani-Mohammadi, S., Amnieh, H. B., & Bahadori, M. (2011). Predicting ground vibration caused by blasting operations in Sarcheshmeh copper mine considering the charge type by Adaptive Neuro-Fuzzy Inference System (ANFIS). Archives of Mining Sciences, 56(4), 701-710.
[9]. Kamali, M., & Ataei, M. (2010). Prediction of blast induced ground vibrations in Karoun III power plant and dam: a neural network. Journal of the Southern African Institute of Mining and Metallurgy, 110(8), 481-490.
[10]. Kamali, M., & Ataei, M. (2011). Prediction of blast induced vibrations in the structures of Karoun III power plant and dam. Journal of Vibration and Control, 17(4), 541-548.
[11]. Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K., & Khandelwal, M. (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental earth sciences, 74, 2845-2860.
[12]. Akyildiz, O., & Hudaverdi, T. (2020). ANFIS modelling for blast fragmentation and blast-induced vibrations considering stiffness ratio. Arabian Journal of Geosciences, 13(21), 1162.
[13]. Hasanipanah, M., Monjezi, M., Shahnazar, A., Armaghani, D. J., & Farazmand, A. (2015). Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement, 75, 289-297.
[14]. Dindarloo, S. R. (2015). Peak particle velocity prediction using support vector machines: a surface blasting case study. Journal of the Southern African Institute of Mining and Metallurgy, 115(7), 637-643.
[15]. Monjezi, M., Baghestani, M., Shirani Faradonbeh, R., Pourghasemi Saghand, M., & Jahed Armaghani, D. (2016). Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Engineering with Computers, 32, 717-728.
[16]. Faradonbeh, R. S., & Monjezi, M. (2017). Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms. Engineering with Computers, 33, 835-851.
[17]. Ataei, M., & Sereshki, F. (2017). Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm. Journal of Mining and Environment, 8(2), 291-304.
[18]. Mohammadi, D., Mikaeil, R., & Abdollahei Sharif, J. (2020). Investigating and ranking blasting patterns to reduce ground vibration using soft computing approaches and MCDM technique. Journal of Mining and Environment, 11(3), 881-897.
[19]. Komadja, G. C., Rana, A., Glodji, L. A., Anye, V., Jadaun, G., Onwualu, P. A., & Sawmliana, C. (2022). Assessing ground vibration caused by rock blasting in surface mines using machine-learning approaches: A comparison of CART, SVR and MARS. Sustainability, 14(17), 11060.
[20]. Nguyen, H., Bui, X. N., & Topal, E. (2023). Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks. International Journal of Coal Geology, 275, 104294.
[21]. Chandrahas, N. S., Choudhary, B. S., Teja, M. V., Venkataramayya, M. S., & Prasad, N. K. (2022). XG boost algorithm to simultaneous prediction of rock fragmentation and induced ground vibration using unique blast data. Applied Sciences, 12(10), 5269.
[22]. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
[23]. Vapnik, V. (1998). Statistical learning theory. John Wiley & Sons google schola, 2, 831-842.
[24]. Schiilkop, P. B., Burgest, C., & Vapnik, V. (1995, August). Extracting support data for a given task. In Proceedings, First International Conference on Knowledge Discovery & Data Mining. AAAI Press, Menlo Park, CA (pp. 252-257).
[25]. Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (Vol. 2, No. 3, p. 4). Cambridge, MA: MIT press.
[26]. Olofsson, S. O. (1990). Applied explosives technology for construction and mining. (No Title).
[27]. Hudaverdi, T., & Akyildiz, O. (2021). An alternative approach to predict human response to blast induced ground vibration. Earthquake engineering and engineering vibration, 20, 257-273.
[28]. Özgül, N. (2012). Stratigraphy and some structural features of the İstanbul Paleozoic. Turkish Journal of Earth Sciences, 21(6), 817-866.
[29]. Basu, D., & Sen, M. (2005, July). Blast induced ground vibration norms—a critical review. In National Seminar on Policies, Statutes & Legislation in Mines (pp. 112-113).
[30]. Duvall, W. I., & Petkof, B. (1959). Spherical propagation of explosion-generated strain pulses in rock (No. 5481-5485). US Department of the Interior, Bureau of Mines.
[31]. Dao, H., Pham, T. L., & Hung, N. P. (2021). Study on an online vibration measurement system for seismic waves caused by blasting for mining in Vietnam. Journal of Mining and Environment, 12(2), 313-325.
[32]. Langefors, U., & Kihlström, B. (1963). The modern technique of rock blasting. (No Title).
[33]. Stagg, K. G., & Zienkiewicz, O. C. (1968). Rock mechanics in engineering practice. (No Title).
[34]. Standard, I. (1973). Criteria for Safety and Design of Structures Subjected to Underground Blast, ISI. IS-6922.
[35]. Roy, P. P. (1993). Putting ground vibration predictions into practice. Colliery Guardian;(United Kingdom), 241(2).
[36]. Mohamadnejad, M., Gholami, R., & Ataei, M. (2012). Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunnelling and Underground Space Technology, 28, 238-244.
[37]. Ataei, M., & Kamali, M. (2013). Prediction of blast-induced vibration by adaptive neuro-fuzzy inference system in Karoun 3 power plant and dam. Journal of Vibration and Control, 19(12), 1906-1914.
[38]. Agrawal, H., & Mishra, A. K. (2019). Modified scaled distance regression analysis approach for prediction of blast-induced ground vibration in multi-hole blasting. Journal of Rock Mechanics and Geotechnical Engineering, 11(1), 202-207.
[39]. Khan, M. F. H., Hossain, M. J., Ahmed, M. T., Monir, M. U., Rahman, M. A., Sweety, T. S., ... & Shovon, S. M. (2025). Ground vibration effect evaluation due to blasting operations. Heliyon, 11(2).
[40]. IBM Corporation. (2016). IBM SPSS Statistics Base 24. Armonk, NY: IBM Corporation.
[41]. Kalaycı, Ş. (2010). SPSS uygulamalı çok değişkenli istatistik teknikleri (Vol. 5, p. 359). Ankara, Turkey: Asil Yayın Dağıtım.
[42]. OLIVER. NELLES. (2020). NONLINEAR SYSTEM IDENTIFICATION: From Classical Approaches to Neural Networks, Fuzzy Systems,... and Gaussian Processes. SPRINGER NATURE.
[43]. Özkan, İ., Ciniviz, M., & Candan, F. (2015). Estimating Engine Performance and Emission Values Using ANFIS/ANFIS Kullanılarak Motor Performans ve Emisyon Değerleri Tahmini. International Journal of Automotive Engineering and Technologies, 4(1), 63-67.
[44]. Tsukamoto, Y. (1979). An approach to fuzzy reasoning method. Advances in fuzzy set theory and applications.
[45]. Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on systems, man, and cybernetics, 20(2), 404-418.
[46]. Takagi, T., & Sugeno, M. (1983). Derivation of fuzzy control rules from human operator's control actions. IFAC proceedings volumes, 16(13), 55-60.
[47]. Yılmaz, M., Çomaklı, Ö., & Haşıloğlu, A. S. (2002). Kanallarda zamana bağlı zorlanmış ısı taşınımının bulanık-sinir ağı (neuro-fuzzy) ile tahmini. GAP IV. Mühendislik Kongresi Bildiriler Kitabı, 06-08.
[48]. Şişman, Y., & Arzu, A. (2003). A temporal neuro-fuzzy approach for time series analysis (Doctoral dissertation, Middle East Technical University, Department of Computer Engineering). Ankara, Türkiye
[49]. Hudaverdi, T., & Akyildiz, O. (2021). Prediction and evaluation of blast-induced ground vibrations for structural damage and human response. Arabian Journal of Geosciences, 14(5), 378.
[50]. Collazos-Escobar, G. A., Gutiérrez-Guzmán, N., Váquiro, H. A., García-Pérez, J. V., & Cárcel, J. A. (2025). Analysis of Machine Learning Algorithms for the Computer Simulation of Moisture Sorption Isotherms of Coffee Beans. Food and Bioprocess Technology, 1-12.
[51]. Works, M. (2017). Statistics and machine learning Toolbox user’s guide. Matwork Inc.
[52]. Hudaverdi, T. (2022). Prediction of flyrock throw distance in quarries by variable selection procedures and ANFIS modelling technique. Environmental Earth Sciences, 81(10), 281.
[53]. Srivastava, A., Choudhary, B. S., & Sharma, M. (2021). A comparative study of machine learning methods for prediction of blast-induced ground vibration. Journal of Mining and Environment, 12(3), 667-677.
[54]. Molavi Nojumi, M., Huang, Y., Hashemian, L., & Bayat, A. (2022). Application of machine learning for temperature prediction in a test road in Alberta. International Journal of Pavement Research and Technology, 15(2), 303-319.
[55]. Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2020). Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers, 36(1), 29-42.
[56]. MathWorks. (n.d.). Support vector machine regression. Retrieved February 10, 2022, from https://www.mathworks.com/help/stats/support-vector-machine-regression.html
[57]. Ghasemi, E., Ataei, M., & Hashemolhosseini, H. (2013). Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. Journal of Vibration and Control, 19(5), 755-770.
)