[1]. Nurhasan, U. and Saputra, P.Y. (2018). Analysis of Sand Mining Areas in Lumajang Using WEBGIS. In 2018 International Conference on Energy and Mining Law (ICEML 2018), Atlantis Press. pp. 350-355.
[2]. Onederra, I., Mardones, F., and Scherpenisse, C. (2010). Application of stochastic approach to blast fragmentation modelling. Transactions of the Institutions of Mining and Metallurgy, Section A: Mining Technology, 119(4), 221–232.
[3]. Shamsi, R., Amini, M.S., Dehghani, H., BascomptaMassanes, M., Shokri, B.J., and Entezam, S. (2022). Prediction of fly-rock using gene expression programming and teaching–learning-based optimization algorithm. Journal of mining and environment, 13 (2): 391-406.
[4]. Enayatollahi, I., AghajaniBazzazi, A., and Asadi, A. (2014). Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock mechanics and rock Engineering, 47 (2): 799-807.
[5]. Nikkhah, A., Vakylabad, A. B., Hassanzadeh, A., Niedoba, T., and Surowiak, A. (2022). An Evaluation on the Impact of Ore Fragmented by Blasting on Mining Performance. Minerals, 12 (2): 258.
[6]. Monjezi, M., Amiri, H., Farrokhi, A., and Goshtasbi, K. (2010). Prediction of rock fragmentation due to blasting in Sarcheshmeh copper mine using artificial neural networks. Geotechnical and Geological Engineering, 28 (4): 423–430.
[7]. Mutinda, E.K., Alunda, B.O., Maina, D.K., and Kasomo, R.M. (2021). Prediction of rock fragmentation using the Kuznetsov-Cunningham-Ouchterlony model. Journal of the Southern African Institute of Mining and Metallurgy, 121 (3): 107–112.
[8]. Bahrami, A., Monjezi, M., Goshtasbi, K., and Ghazvinian, A. (2011). Prediction of rock fragmentation due to blasting using artificial neural network. Engineering with Computers, 27 (2): 177–181.
[9]. Amoako, R., Jha, A., and Zhong, S. (2022). Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach. Mining, 2 (2): 233–247.
[10]. BakhshandehAmnieh, H., HakimiyanBidgoli, M., Mokhtari, H., andAghajaniBazzazi, A. (2019). Application of simulated annealing for optimization of blasting costs due to air overpressure constraints in open-pit mines. Journal of Mining and Environment, 10 (4): 903-916.
[11]. Zhang, X., Nguyen, H., Bui, X.N., Tran, Q.H., Nguyen, D.A., Bui, D.T., andMoayedi, H. (2020). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research, 29 (2): 711-721.
[12]. Singh, T. N.and Singh, V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical and Geological Engineering, 23 (3): 249-262.
[13]. Taiwo, B.O. (2022). Effect of charge load proportion and blast controllable factor design on blast fragment size distribution Effect of charge load proportion and blast controllable factor design on blast fragment size distribution. August.
[14]. Hudaverdi, T., Kuzu, C., andFisne, A. (2012). Investigation of the blast fragmentation using the mean fragment size and fragmentation index. International Journal of Rock Mechanics and Mining Sciences, 56, 136-145.
[15]. Schleifer, J.and Tessier, B. (2018). FRAGSCAN: A tool to measure fragmentation of blasted rock. In Measurement of blast fragmentation (pp. 73-78). Routledge.
[16]. Hunter, G.C., McDermott, C., Miles, N.J., Singh, A., andScoble, M.J. (1990). A review of image analysis techniques for measuring blast fragmentation. Mining Science and Technology, 11(1): 19-36.
[17]. Kulatilake, P.H. S.W., Qiong, W., Hudaverdi, T., andKuzu, C. (2010). Mean particle size prediction in rock blast fragmentation using neural networks. Engineering Geology, 114 (3-4): 298-311.
[18]. Kulatilake, P.H.S.W., Hudaverdi, T., and Wu, Q. (2012). New prediction models for mean particle size in rock blast fragmentation. Geotechnical and Geological Engineering, 30 (3): 665-684.
[19]. Morin, M. A.andFicarazzo, F. (2006). Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. Computers and geosciences, 32 (3): 352-359.
[20]. Kleine, T.H., and Cameron, A.R. (2018). Blast fragmentation measurement using GoldSize. In Measurement of Blast Fragmentation (pp. 83-89). Routledge.
[21]. Hudaverdi, T., Kulatilake, P.H.S.W., and Kuzu, C. (2011). Prediction of blast fragmentation using multivariate analysis procedures. International Journal for Numerical and Analytical Methods in Geomechanics, 35(12): 1318-1333.
[22]. Shi, X.Z., Jian, Z.H.O.U., Wu, B.B., Huang, D., and Wei, W.E.I. (2012). Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction. Transactions of Nonferrous Metals Society of China, 22(2): 432-441.
[23]. Hartman, H.L. and Mutmansky, J.M. (2002). Introductory mining engineering. John Wiley & Sons.
[24]. Law, T.M., May, J., Spathis, A.T., Du Plessis, A.T., and Palmer, A.M. (2001). Blast damage and blast dilution control: the application of bulk emulsion systems at the WMC St Ives junction mine. Fragblast, 5(1-2): 1-20.
[25]. Bonissone, P.P. (2015). Machine learning applications. Springer Handbook of Computational Intelligence, 783–821.
[26]. Sayadi, A., Monjezi, M., Talebi, N., and Khandelwal, M. (2013). A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak. Journal of Rock Mechanics and Geotechnical Engineering, 5 (4): 318–324.
[27]. Eluyode, O.S. and Akomolafe, D.T. (2013). Comparative study of biological and artificial neural networks. European Journal of Applied Engineering and Scientific Research, 2 (1): 36-46.
[28]. Agatonovic-Kustrin, S.and Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22 (5): 717-727.
[29]. Ke, B., Pan, R., Zhang, J., Wang, W., Hu, Y., Lei, G., Chi, X., Ren, G., and You, Y. (2022). Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine. Minerals, 12 (7).
[30]. Ouchterlony, F.and Sanchidrián, J.A. (2018). A review of the development of better prediction equations for blast fragmentation. Rock Dynamics–Experiments, Theories and Applications, 25-45.
[31]. Li, E., Yang, F., Ren, M., Zhang, X., Zhou, J., and Khandelwal, M. (2021). Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms. Journal of Rock Mechanics and Geotechnical Engineering, 13(6): 1380–1397.
[32]. Hasanipanah, M., Amnieh, H.B., Arab, H., and Zamzam, M.S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30 (4): 1015–1024.
[33]. Shakeri, J., AminiKhoshalan, H., Dehghani, H., Bascompta, M., and Onyelowe, K. (2022). Developing new models for flyrock distance assessment in open-pit mines. Journal of Mining and Environment, 13 (2): 375-389.
[34]. Gao, W., Karbasi, M., Hasanipanah, M., Zhang, X., and Guo, J. (2018). Developing GPR model for forecasting the rock fragmentation in surface mines. Engineering with Computers, 34 (2): 339–345.
[35]. Sayevand, K., Arab, H., and Golzar, S.B. (2018). Development of imperialist competitive algorithm in predicting the particle size distribution after mine blasting. Engineering with Computers, 34 (2): 329–338.
[36]. Mojtahedi, S.F.F., Ebtehaj, I., Hasanipanah, M., Bonakdari, H., and Amnieh, H.B. (2019). Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Engineering with Computers, 35 (1): 47–56.
[37]. Huang, J., Asteris, P.G., Manafi Khajeh Pasha, S., Mohammed, A.S., and Hasanipanah, M. (2022). A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm. Engineering with Computers, 38 (3): 2209–2220.
[38]. Fang, Q., Nguyen, H., Bui, X.N., Nguyen-Thoi, T., and Zhou, J. (2021). Modeling of rock fragmentation by firefly optimization algorithm and boosted generalized additive model. Neural Computing and Applications, 33 (8): 3503–3519.
[39]. Afan, H.A., El-Shafie, A., Yaseen, Z.M., Hameed, M.M., Wan Mohtar, W.H M., and Hussain, A. (2015). ANN Based Sediment Prediction Model Utilizing Different Input Scenarios. Water Resources Management, 29 (4): 1231–1245.
[40]. Liu, L., Zhou, W., and Gutierrez, M. (2022). Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets. Journal of Rock Mechanics and Geotechnical Engineering, 14(4): 1028–1041.
[41]. Saeidi, O., Torabi, S. R., and Ataei, M. (2014). Prediction of the rock mass diggability index by using fuzzy clustering-based, ANN and multiple regression methods. Rock Mechanics and Rock Engineering, 47 (2): 717–732.
[42]. Shakeri, J., Khoshalan, H.A., Dehghani, H., Bascompta, M., and Onyelowe, K. (2022). Developing New Models for Flyrock Distance Assessment in Open-Pit Mines. Journal of Mining and Environment, 13 (2): 377–391.
[43]. Alamdari, S., Hossein Basiri, M., Mousavi, A., and Soofastaei, A. (2022). Application of Machine Learning Techniques to Predict Haul Truck Fuel Consumption in Open-Pit Mines. Journal of Mining and Environment, 13 (1): 69–85.
[44]. Bastami, R., Aghajani Bazzazi, A., Hamidian Shoormasti, H., and Ahangari, K. (2020). Prediction of Blasting Cost in Limestone Mines using Gene Expression Programming Model and Artificial Neural Networks. Journal of Mining and Environment, 11 (1): 281–300.
[45]. Mikaeil, R., Piri, M., Shaffiee Haghshenas, S., Careddu, N., and Hashemolhosseini, H. (2022). An Experimental-Intelligent Method to Predict Noise Value of Drilling in Dimension Stone Industry. Journal of Mining and Environment, 13 (3): 693–713.
[46]. Fathi, M., Alimoradi, A., and Ahooi, H. H. (2021). Optimizing Extreme Learning Machine Algorithm using Particle Swarm Optimization to Estimate Iron Ore Grade. Journal of Mining and Environment, 12 (2): 397–411.
[47]. Taiwo, B.O. (2022). Improvement of Small-Scale Dolomite Blasting Productivity: Comparison of Existing Empirical Models with Image Analysis Software and Artificial Neural Network Models. Journal of Mining and Environment, 13 (3): 627–641.
[48]. Sirjani, A.K., Sereshki, F., Ataei, M., and Hosseini, M.A. (2022). Prediction of Backbreak in the Blasting Operations using Artificial Neural Network (ANN) Model and Statistical Models (Case study: Gol-e-Gohar Iron Ore Mine No. 1). Archives of Mining Sciences, 67 (1): 107–121.
[49]. ISRM (2007). Suggested Methods for Testing or Measuring Properties of Rocks and Rock Masses in the Laboratory and In Situ, as Well as for Monitoring the Performance of Rock Engineering Structures, International Society of Rock Mechanics and Rock Engineering.
[50]. ISRM (1979a) International Society of Rock Mechanics, Commission on Standardization of Laboratory and field tests, Suggested method for determination of the uniaxial compressive strength of Rock Materials, International Journal of Rock Mechanics and mining science and Geomechanics Abstract, Vol. 16 (2): pp.135-140.
[51]. Shehu, S.A., Yusuf, K.O., and Hashim, M.H.M. (2022). Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating. Geomechanics and Geoengineering, 17 (1): 197-205.
[52]. Maerz, N.H., Palangio, T.C., and Franklin, J.A. (2018). WipFrag image based granulometry system. In Measurement of Blast Fragmentation (pp. 91-99). Routledge.
[53]. Jayalakshmi, T., and Santhakumaran, A. (2011). Statistical normalization and back-propagation for classification. International Journal of Computer Theory and Engineering, 3 (1): 1793-8201.
[54]. Chen, H.and Zeng, Z. (2013). Deformation prediction of landslide based on improved back-propagation neural network. Cognitive computation, 5 (1): 56-62.
[55]. Al-Bakri, A.Y.and Sazid, M. (2021). Application of Artificial Neural Network (ANN) for Prediction and Optimization of Blast-Induced Impacts. Mining, 1 (3): 315-334.
[56]. Mahdevari, S., Shahriar, K., Yagiz, S., andShirazi, M.A. (2014). A support vector regression model for predicting tunnel boring machine penetration rates. International Journal of Rock Mechanics and Mining Sciences, 72, 214-229.
[57]. Tiile, R.N. (2016). Artificial neural network approach to predict blast-induced ground vibration, airblast and rock fragmentation. Missouri University of Science and Technology.
[58]. Singh, S.P.and Abdul, H. (2013). Investigation of blast design parameters to optimize fragmentation. In Rock Fragmentation by Blasting: The 10th International Symposium on Rock Fragmentation by Blasting, 2012 (Fragblast 10) (pp. 181-186). Taylor and Francis Books Ltd.
[59]. Thornton, D., Kanchibotla, S.S., and Brunton, I. (2002). Modelling the impact of rockmass and blast design variation on blast fragmentation. Fragblast, 6 (2): 169-188.
[60]. Dotto, M.S., Pourrahimian, Y., Joseph, T., and Apel, D. (2022). Assessment of blast energy usage and induced rock damage in hard rock surface mines. CIM Journal, 1-15.
[61]. Silva, J., Worsey, T., and Lusk, B. (2019). Practical assessment of rock damage due to blasting. International Journal of Mining Science and Technology, 29 (3): 379-385.
[62]. GhanizadehZarghami, A., Shahriar, K., Goshtasbi, K., and Akbari Dehkharghani, A. (2020). A New Technical and Economic Model to Calculate Specific Charge and Specific Drilling Using Hole Diameter, Bench Height, Uniaxial Compressive Strength, and Joint Set Orientation. Journal of Mining and Environment, 11 (4): 991-1005.
[63]. Roy, M.P., Paswan, R.K., Sarim, M.D., Kumar, S., Jha, R., and Singh, P.K. (2016). Rock fragmentation by blasting-A review. Journal of mines, metals and fuels, 64(9): 424-431.
[64]. BakhsandehAmnieh, H., Mohammadi, A., and Mozdianfard, M. (2013). Predicting peak particle velocity by artificial neural networks and multivariate regression analysis-Sarcheshmeh copper mine, Kerman, Iran. Journal of Mining and Environment, 4 (2): 125-132.
[65]. Ghasemi, E., Amini, H., Ataei, M., and Khalokakaei, R. (2014). Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation. Arabian Journal of Geosciences, 7 (1): 193-202.
[66]. Saeidi, O., Torabi, S.R., and Ataei, M. (2014). Prediction of the rock mass diggability index by using fuzzy clustering-based, ANN and multiple regression methods. Rock mechanics and rock engineering, 47 (2): 717-732.
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