Machine learning based prediction of flyrock distance in rock blasting: A safe and sustainable mining approach

Abstract

Flyrock is a significant environmental and safety concern in mining and construction. It arises from various geological and blast design factors, posing risks to workers, machinery, and nearby structures. This study examined how these factors affect the rate and distance of flyrock projections caused by blasts. To address this issue, advanced machine learning (ML) models were used to predict flyrock distances in the Akoko Edo dolomite quarries. The models examined included bidirectional recurrent neural networks (BRNNs), support vector regression (SVR) with different kernels (SVR-S, SVR-RBF, SVR-L, SVR-P), long short-term memory (LSTM) networks, and random forest (RF) algorithms. A case study was conducted using 258 blasting data samples to develop these models. Key factors influencing flyrock were identified: blast hole burden distance, maximum instantaneous charge, and rock brittleness index. Using these factors, a flyrock possibility assessment chart was created to enhance the safety of small-scale mining operations. The model’s prediction accuracy was evaluated using correlation coefficients and four performance metrics. The LSTM model stood out, achieving the highest coefficient of correlation (R² = 0.99) for both training and testing datasets. This indicates that the LSTM model accurately predicts blast-induced flyrock distance. The study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants (SVR-S, SVR-L, and SVR-P). In conclusion, the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

Keywords

Blasting / Flyrock / Safety chart / Mine production reliability / Long short-term memory networks

References

[1] A. Ur Rehman, M.Z. Emad, M.U. Khan, Improving the environmental and economic aspects of blasting in surface mining by using stemming plugs, J. S. Afr. Inst. Min. Metall. 121 (7) (2021) 369-378.

[2] P.P. Roy, Rock Blasting: Effects and Operation, Taylor & Francis, 2005.

[3] V.K. Singh, D. Singh, T.N. Singh, Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks, Int. J. Rock. Mech. Min. Sci. 38 (2) (2001) 269-284.

[4] R. Trivedi, T.N. Singh, N. Gupta, Prediction of blast-induced flyrock in opencast mines using ANN and ANFIS, Geotech. Geol. Eng. 33 (4) (2015) 875-891.

[5] S. Bhandari, Engineering Rock Blasting Operations, A.A. Balkema, Rotterdam, Netherlands, 1997.

[6] T.S. Bajpayee, T.R. Rehak, G.L. Mowrey, D.K. Ingram, Blasting injuries in surface mining with emphasis on flyrock and blast area security, J. Saf. Res. 35 (1) (2004) 47-57.

[7] M. Hajihassani, D. Jahed Armaghani, M. Monjezi, E.T. Mohamad, A. Marto, Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach, Environ. Earth Sci. 74 (4) (2015) 2799-2817.

[8] B.S. Choudhary, A. Agrawal, Minimization of blast-induced hazards and efficient utilization of blast energy by implementing a novel stemming plug system for eco-friendly blasting in open pit mines, Nat. Resour. Res. 31 (6) (2022) 3393-3410.

[9] A.K. Raina, R.M. Bhatawdekar, Blast-induced flyrock: Risk evaluation and management, in: T. Roshni, P. Samui, D.T. Bui, D. Kim, R. Khatibi Risk (Eds.), Reliability and Sustainable Remediation in the Field of Civil and Environmental Engineering, Elsevier, 2022, pp. 209-247.

[10] J.L.V. Mariz, A. Soofastaei, Advanced analytics for rock blasting and explosives engineering in mining, in: A. Soofastaei (Ed.), Advanced Analytics in Mining Engineering, Springer, Cham, 2022, pp. 363-477.

[11] D.J. Armaghani, A. Mahdiyar, M. Hasanipanah, R.S. Faradonbeh, M. Khandelwal, H.B. Amnieh, Risk assessment and prediction of flyrock distance by combined multiple regression analysis and Monte Carlo simulation of quarry blasting, Rock Mech. Rock Eng. 49 (9) (2016) 3631-3641.

[12] F. Faramarzi, H. Mansouri, M. Ali Ebrahimi Farsangi, Development of rock engineering systems-based models for flyrock risk analysis and prediction of flyrock distance in surface blasting, Rock Mech. Rock Eng. 47 (4) (2014) 1291-1306.

[13] A. Bala, An assessment of drilling and blasting techniques at granite quarry of AFDIN construction company limited, Katsina state, Nigeria, Int. J. Environ. Geoinformatics 8 (4) (2021) 450-459.

[14] T. Bamgbose, O. Omisore, A. Ademola, O.B. Oyesola, Challenges of quarry activities among rural dwellers in Odeda local government area of Ogun state, Res. J. Agric. Environ. Manag. 3 (1) (2014) 49-55.

[15] T.S. Bajpayee, S.K. Bhatt, T.R. Rehak, G. Engineer, G.L. Mowrey, D.K. Ingram, Fatal accidents due to flyrock and lack of blast area security and working practices in mining, J. Mines Met. Fuels 51 (11) (2003) 344-349.

[16] T.Y. Baah-Ennumh, A.S. Yeboah, A.E.J. Akularemi, Contextualizing the effects of stone quarrying: insights from the Wenchi municipality in Ghana, GeoJournal 86 (1) (2021) 489-505.

[17] M. Monjezi, A. Mehrdanesh, A. Malek, M. Khandelwal, Evaluation of effect of blast design parameters on flyrock using artificial neural networks, Neural Comput. Appl. 23 (2) (2013) 349-356.

[18] T. Laing, S. Moonsammy, Evaluating the impact of small-scale mining on the achievement of the sustainable development goals in Guyana, Environ. Sci. Policy 116 (2021) 147-159.

[19] A.K. Raina, V.M.S.R. Murthy, A.K. Soni, Flyrock in surface mine blasting: understanding the basics to develop a predictive regime, Curr. Sci. Assoc. 108 (4) (2015) 660-665.

[20] R.M. Bhatawdekar, D.J. Armaghani, A. Azizi, Applications of AI and ML techniques to predict backbreak and flyrock distance resulting from blasting, in: R.M. Bhatawdekar, D.J. Armaghani, A. Azizi (Eds.), Environmental Issues of Blasting: Applications of Artificial Intelligence Techniques, Springer, Singapore, 2021, pp. 41-59.

[21] E.T. Mohamad, C.S. Yi, B.R. Murlidhar, R. Saad, Effect of geological structure on flyrock prediction in construction blasting, Geotech. Geol. Eng. 36 (4) (2018) 2217-2235.

[22] A. Abd Elwahab, E. Topal, H.D. Jang, Review of machine learning application in mine blasting, Arab. J. Geosci. 16 (2) (2023) 133.

[23] L.F. Velasquez, Prediction of Blast-induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches (Dissertation), University of Kentucky, Kentucky, USA, 2023.

[24] M. Monjezi, A. Bahrami, A. Yazdian Varjani, Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks, Int. J. Rock Mech. Min. Sci. 47 (3) (2010) 476-480.

[25] E. Ghasemi, M. Sari, M. Ataei, Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines, Int. J. Rock Mech. Min. Sci. 52 (2012) 163-170.

[26] F.Z. Meng, L.N.Y. Wong, H. Zhou, Rock brittleness indices and their applications to different fields of rock engineering: A review, J. Rock Mech. Geotech. Eng. 13 (1) (2021) 221-247.

[27] M. Monjezi, H. Amini Khoshalan, A. Yazdian Varjani, Prediction of flyrock and backbreak in open pit blasting operation: A neuro-genetic approach, Arab. J. Geosci. 5 (3) (2012) 441-448.

[28] U. Langefors, B. Kihlström, The Modern Technique of Rock Blasting, 3rd ed., Wiley, New York, 1979.

[29] J. Roth, A model for the determination of flyrock range as a function of shot conditions, The National Institute for Occupational Safety and Health (NIOSH), 1979.

[30] R.M. Bhatawdekar, R. Kumar, M.M. Sabri Sabri, B. Roy, E.T. Mohamad, D. Kumar, S. Kwon, Estimating flyrock distance induced due to mine blasting by extreme learning machine coupled with an equilibrium optimizer, Sustainability 15 (4) (2023) 3265.

[31] A. Graves, A.R. Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks, in: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada, 2013, pp. 6645-6649.

[32] I. Sutskever, Q. Vinyals, Q.V. Le, Sequence to sequence learning with neural networks, in: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014.

[33] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9 (8) (1997) 1735-1780.

[34] Z. Fang, X. Ma, H.F. Pan, G.B. Yang, G.R. Arce, Movement forecasting of financial time series based on adaptive LSTM-BN network, Expert Syst. Appl. 213 (2023) 119207.

[35] H. Sak, A. Senior, F. Beaufays, Long short-term memory recurrent neural network architectures for large scale acoustic modeling, in: Proc. Interspeech 2014, Singapore, 2014, pp. 338-342.

[36] J.K. Lee, S.S. Lee, Predicting the geological condition beyond the tunnel excavation face using MSP monitoring data and LSTM algorithm, IOP Conf. Ser.: Earth Environ. Sci. 1124 (1) (2023) 012007.

[37] G.I. Drewil, R.J. Al-Bahadili, Air pollution prediction using LSTM deep learning and metaheuristics algorithms, Meas. Sens. 24 (2022) 100546.

[38] K. Greff, R.K. Srivastava, J. Koutník, B.R. Steunebrink, J. Schmidhuber, LSTM: a search space odyssey, IEEE Trans. Neural Netw. Learn. Syst. 28 (10) (2017) 2222-2232.

[39] Y. Bengio, P. Simard, P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE Trans. Neural Netw. 5 (2) (1994) 157-166.

[40] M. Varsha, B. Poornima, H.P. Vinutha, M.P. Pavan Kumar, Predictive model for rice blast disease on climate data using long short-term memory and multi-layer perceptron: an empirical study on davangere district, Ann. Rom. Soc. Cell Biol. 25 (6) (2021) 4703-4722.

[41] A.I. Lawal, O.J. Ojo, M. Kim, S. Kwon, Determination of blast-induced flyrock using a drone technology: a bibliometric overview with practical soft computing implementation, Arab. J. Geosci. 15 (19) (2022) 1581.

[42] A. Siamaki, Advanced analytics for drilling and blasting, in: A. Soofastaei (Ed.), Advanced Analytics in Mining Engineering: Leverage Advanced Analytics in Mining Industry to Make Better Business Decisions, Springer, Cham, 2022, pp. 323-343.

[43] R.F. Chiappetta, M.E. Mammele, Use of high-speed motion picture photography in blast evaluation and design, in: Proceedings Volume 0832, High Speed Photography, Videography, and Photonics V, San Diego, CA, USA, 1988.

[44] N. Lundborg, N. Persson, A. Ladegaard-Pedersen, R. Holmberg, Keeping the lid on flyrock in open pit blasting, Eng. Min. J. 176 (1957) 95-100.

[45] R. Trivedi, T.N. Singh, A.K. Raina, Prediction of blast-induced flyrock in Indian limestone mines using neural networks, J. Rock. Mech. Geotech. Eng. 6 (5) (2014) 447-454.

[46] K. Manoj, M. Monjezi, Prediction of flyrock in open pit blasting operation using machine learning method, Int. J. Min. Sci. Technol. 23 (3) (2013) 313-316.

[47] A. Marto, M. Hajihassani, D. Jahed Armaghani, E. Tonnizam Mohamad, A.M. Makhtar, A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network, Sci. World J. 2014 (2014) 643715.

[48] E. Ghasemi, H. Amini, M. Ataei, R. Khalokakaei, Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation, Arab. J. Geosci. 7 (1) (2014) 193-202.

[49] A. Zangoei, M. Monjezi, D.J. Armaghani, A. Mehrdanesh, S. Ahmadian, Prediction and optimization of flyrock and oversize boulder induced by mine blasting using artificial intelligence techniques, Environ. Earth Sci. 81 (13) (2022) 359.

[50] M.S. Barkhordari, D.J. Armaghani, P. Fakharian, Ensemble machine learning models for prediction of flyrock due to quarry blasting, Int. J. Environ. Sci. Technol. 19 (9) (2022) 8661-8676.

[51] P. Sorabi, M. Ataei, M.R.A. Jazi, H. Dehghani, J. Shakeri, M.H. Habibi, Utilizing heuristic strategies for predicting the backbreak occurrences in open-pit mines, Gol Gohar Mine, Iran, Soft Comput. (2024), https://doi.org/10.1007/s00500-023-09613-8.

[52] R.N. Gupta, A. Bagchi, B. Singh, Optimising drilling and blasting parameters to improve blasting efficiency, Status Report, CBIP Rock Mech, New Delhi, India, 1988, pp. 185-206.

[53] A.B. Richard, A.J. Moore, Kalgoorlie consolidated gold mines flyrock model calibration update, TERROCK Consulting Engineers Report, 2006.

[54] C.K. McKenzie, Flyrock range and fragment size prediction, in: Proceedings of the 35th Annual Conference on Explosives and Blasting Technique Vol. 2, International Society of Explosives Engineers, Cleveland, OH, USA, 2009.

[55] M. Rezaei, M. Monjezi, A. Yazdian Varjani, Development of a fuzzy model to predict flyrock in surface mining, Saf. Sci. 49 (2) (2011) 298-305.

[56] A. Bahrami, M. Monjezi, K. Goshtasbi, A. Ghazvinian, Prediction of rock fragmentation due to blasting using artificial neural network, Eng. Comput. 27 (2) (2011) 177-181.

[57] H. Amini, R. Gholami, M. Monjezi, S.R. Torabi, J. Zadhesh, Evaluation of flyrock phenomenon due to blasting operation by support vector machine, Neural Comput. Appl. 21 (8) (2012) 2077-2085.

[58] E.T. Mohamad, D.J. Armaghani, M. Hajihassani, K. Faizi, A. Marto, A simulation approach to predict blasting-induced flyrock and size of thrown rocks, Electron. J. Geotech. Eng. 18 (B) (2013) 365-374.

[59] R.S. Faradonbeh, D.J. Armaghani, M. Monjezi, E.T. Mohamad, Genetic programming and gene expression programming for flyrock assessment due to mine blasting, Int. J. Rock. Mech. Min. Sci. 88 (2016) 254-264.

[60] A.K. Raina, V.M.S.R. Murthy, Prediction of flyrock distance in open pit blasting using surface response analysis, Geotech. Geol. Eng. 34 (1) (2016) 15-28.

[61] M. Hasanipanah, D. Jahed Armaghani, H. Bakhshandeh Amnieh, M.Z.A. Majid, M.M.D. Tahir, Application of PSO to develop a powerful equation for prediction of flyrock due to blasting, Neural Comput. Appl. 28 (1) (2017) 1043-1050.

[62] P.F. Asl, M. Monjezi, J.K. Hamidi, D.J. Armaghani, Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm, Eng. Comput. 34 (2) (2018) 241-251.

[63] M. Hasanipanah, D.J. Armaghani, H.B. Amnieh, M. Koopialipoor, H. Arab, A risk-based technique to analyze flyrock results through rock engineering system, Geotech. Geol. Eng. 36 (2018) 2247-2260.

[64] H.N. Rad, M. Hasanipanah, M. Rezaei, A.L. Eghlim, Developing a least squares support vector machine for estimating the blast-induced flyrock, Eng. Comput. 34 (4) (2018) 709-717.

[65] B.R. Murlidhar, H. Nguyen, J. Rostami, X. Bui, D.J. Armaghani, P. Ragam, E.T. Mohamad, 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. 13 (6) (2021) 1413-1427.

[66] S. Hosseini, A. Mousavi, M. Monjezi, M. Khandelwal, Mine-to-crusher policy: planning of mine blasting patterns for environmentally friendly and optimum fragmentation using Monte Carlo simulation-based multi-objective grey wolf optimization approach, Resour. Policy 79 (2022) 103087.

[67] S. Hosseini, R. Poormirzaee, M. Hajihassani, R. Kalatehjari, An ANN-fuzzy cognitive map-based Z-number theory to predict flyrock induced by blasting in open-pit mines, Rock Mech. Rock Eng. 55 (7) (2022) 4373-4390.

[68] J. Ye, X. He, A novel hybrid of ANFIS-based models using optimisation approaches to predict mine blast-induced flyrock, Int. J. Environ. Sci. Technol. 20 (4) (2023) 3673-3686.

[69] M. Yari, D.J. Armaghani, C. Maraveas, A.N. Ejlali, E.T. Mohamad, P.G. Asteris, Several tree-based solutions for predicting flyrock distance due to mine blasting, Appl. Sci. 13 (3) (2023) 1345.

[70] R. Ulusay, The ISRM Suggested Methods for Rock Characterization, Testing and Monitoring: 2007-2014, Springer, Cham, 2014.

[71] R.M. Goktan, N.G. Yilmaz, A new methodology for the analysis of the relationship between rock brittleness index and drag pick cutting efficiency, J. South. Afr. Inst. Min. Metall. 105 (10) (2005) 727-733.

[72] Y.J. Fan, K.K. Xu, H. Wu, Y. Zheng, B. Tao, Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network, IEEE Access 8 (2020) 25111-25121.

[73] F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev. 65 (6) (1958) 386-408.

[74] F.K. do Carmo Félix, L.A. Letti, G. Vinícius de Melo Pereira, P.G.B. Bonfim, V.T. Soccol, C.R. Soccol, L-lysine production improvement: a review of the state of the art and patent landscape focusing on strain development and fermentation technologies, Crit. Rev. Biotechnol. 39 (8) (2019) 1031-1055.

[75] B.O. Taiwo, Improvement of small-scale dolomite blasting productivity: comparison of existing empirical models with image analysis software and artificial neural network models, J. Min. Environ. 13 (3) (2022) 627-641.

[76] A.I. Ajibona, B.O. Taiwo, T.B. Afeni, V.A. Akinbinu, O. Emmanuel, O.B. Ogunyemi, Development of efficient empirical models for the prediction of oil well fracture pressure gradient, Daffodil Int. Univ. J. Sci. Technol. 17 (2) (2022) 16-23.

[77] A. Onan, Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification, J. King Saud. Univ. Comput. Inf. Sci. 34 (5) (2022) 2098-2117.

[78] F.Y. Osisanwo, J.E.T. Akinsola, O. Awodele, J.O. Hinmikaiye, O. Olakanmi, J. Akinjobi, Supervised machine learning algorithms: classification and comparison, Int. J. Comput. Trends Technol. 48 (3) (2017) 128-138.

[79] H. Han, D. Jahed Armaghani, R. Tarinejad, J. Zhou, M.M. Tahir, Random forest and Bayesian network techniques for probabilistic prediction of flyrock induced by blasting in quarry sites, Nat. Resour. Res. 29 (2) (2020) 655-667.

[80] H. Nguyen, X.N. Bui, Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest, Nat. Resour. Res. 28 (3) (2019) 893-907.

[81] Z. Yu, X.Z. Shi, J. Zhou, X. Chen, X.Y. Qiu, Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris Hawks optimization algorithm, Appl. Sci. 10 (4) (2020) 1403.

[82] J. Kumar, R. Goomer, A.K. Singh, Long short term memory recurrent neural network (LSTM-RNN) based workload forecasting model for cloud datacenters, Procedia Comput. Sci. 125 (2018) 676-682.

[83] M.A. Al Mehedi, M. Khosravi, M.M.S. Yazdan, H. Shabanian, Exploring temporal dynamics of river discharge using univariate long short-term memory (LSTM) recurrent neural network at east branch of Delaware River, Hydrology 9 (11) (2022) 202.

[84] P.C. Sen, M. Hajra, M. Ghosh, Supervised classification algorithms in machine learning: A survey and review, in: J. Mandal, D. Bhattacharya (Eds.), Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol. 937, Springer, Singapore, 2018, pp. 99-111.

[85] G. Camps-Valls, L. Bruzzone, Kernel-based methods for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens. 43 (6) (2005) 1351-1362.

[86] C. Luo, C. Huang, J. Cao, J.Q. Lu, W. Huang, J.H. Guo, Y. Wei, Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm, Neural Process. Lett. 50 (2019) 2305-2322.

[87] K.E. Taylor, Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.: Atmos. 106 (D7) (2001) 7183-7192.

[88] Y. Fissha, J. Khatti, H. Ikeda, K.S. Grover, N. Owada, H. Toriya, T. Adachi, Y. Kawamura, Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms, Sci. Rep. 14 (1) (2024) 20026.

[89] B.O. Taiwo, G. Angesom, Y. Fissha, Y. Kide, E. Li, K. Haile, O.A. Oni, Artificial neural network modeling as an approach to limestone blast production rate prediction: a comparison of PI-BANN and MVR models, J. Min. Environ. 14 (2) (2023) 375-388.

[90] N.S. Chandrahas, B.S. Choudhary, M.S. Venkataramayya, F. Yewuhalashet, An inventive approach for simultaneous prediction of mean fragmentation size and peak particle velocity using futuristic datasets through improved techniques of genetic XG boost algorithm, Min. Metall. Explor. (2024), https://doi.org/10.1007/s42461-024-01045-8.

[91] A. Rabbani, D.R. Kumar, Y. Fissha, N.P.G. Bhavani, S.K. Ahirwar, S. Sharma, B.K. Saraswat, H. Ikeda, T. Adachi, Optimization of an artificial neural network using four novel metaheuristic algorithms for the prediction of rock fragmentation in mine blasting, J. Inst. Eng. Ind. Ser. D. (2024), https://doi.org/10.1007/s40033-024-00781-x.

[92] B.O. Taiwo, B. Adebayo, Y. Fissha, A.A. Akinlabi, Enhancing rock fragmentation in mining: leveraging ensemble classification machine learning algorithms for blast toe volume assessment, J. Inst. Eng. Ind. Ser. D. (2024), https://doi.org/10.1007/s40033-024-00769-7.

[93] A. Gebretsadik, R. Kumar, Y. Fissha, Y. Kide, N. Okada, H. Ikeda, A.K. Mishra, D.J. Armaghani, Y. Ohtomo, Y. Kawamura, Enhancing rock fragmentation assessment in mine blasting through machine learning algorithms: a practical approach, Discov. Appl. Sci. 6 (5) (2024) 223.

[94] B.O. Taiwo, F. Yewuhalashet, L.B. Adamolekun, O.O. Bidemi, O.V. Famobuwa, A.O. Victoria, Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model, Artif. Intell. Rev. 56 (12) (2023) 14535-14556.

[95] N.S. Chandrahas, Y. Fissha, B.S. Choudhary, B. Olamide Taiwo, M.S. Venkataramayya, T. Adachi, Experimental data-driven algorithm to predict muckpile characteristics in jointed overburden bench using unmanned aerial vehicle and AI tools, Int. J. Min. Reclam. Environ. 38 (8) (2024) 642-676.

[96] B.O. Taiwo, O.V. Famobuwa, M.M. Mata, M. Sazid, Y. Fissha, V.A. Jebutu, O. Abubakar, Granite downstream production dependent size and profitability assessment: an application of mathematical-based artificial intelligence model and WipFrag software, J. Min. Environ. 15 (2) (2024) 497-515.

[97] Y. Fissha, H. Ikeda, H. Toriya, T. Adachi, Y. Kawamura, Application of Bayesian neural network (BNN) for the prediction of blast-induced ground vibration, Appl. Sci. 13 (5) (2023) 3128.

[98] S. Hosseini, J. Khatti, B.O. Taiwo, Y. Fissha, K.S. Grover, H. Ikeda, M. Pushkarna, M. Berhanu, M. Ali, Assessment of the ground vibration during blasting in mining projects using different computational approaches, Sci. Rep. 13 (2023) 18582.

[99] Y. Fissha, H. Ikeda, H. Toriya, N. Owada, T. Adachi, Y. Kawamura, Evaluation and prediction of blast-induced ground vibrations: a Gaussian process regression (GPR) approach, Mining 3 (4) (2023) 659-682.

[100] B.O. Taiwo, Y. Fissha, T. Palangio, A. Palangio, H. Ikeda, N.R. Cheepurupalli, N.M. Khan, A.A. Akinlabi, O.V. Famobuwa, J.O. Faluyi, Y. Kawamura, Assessment of charge initiation techniques effect on blast fragmentation and environmental safety: an application of WipFrag software, Mining 3 (3) (2023) 532-551.

[101] B.O. Taiwo, F. Yewuhalashet, O.B. Ogunyemi, V.A. Babatuyi, E.I. Okobe, E.A. Orhu, Quarry slope stability assessment methods with blast induced effect monitoring in Akoko Edo, Nigeria, Geotech. Geol. Eng. 41 (4) (2023) 2553-2571.