A State of the art Catboost-Based T-Distributed Stochastic Neighbor Embedding Technique to Predict Back-break at Dewan Cement Limestone Quarry | ||
| Journal of Mining and Environment | ||
| مقاله 6، دوره 12، شماره 3، مهر 2021، صفحه 679-691 اصل مقاله (9.29 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2021.11222.2104 | ||
| نویسنده | ||
| M. Kamran* | ||
| Department of Mining Engineering, Institute Technology of Bandung, Indonesia | ||
| چکیده | ||
| The blasting operation is an important rock fragmentation technique employed in several foundation engineering disciplines such as mining, civil, tunneling, and road planning. Back-break (BB) is one of the adverse effects caused by the blasting operations that produces several effects including vulnerability of mining machinery, bench slope design, and risks to the next blast-patterns due to the eruption of gases from several discontinuities in jointed rock masses. Several techniques have been executed by the researchers in order to predict BB in the blasting operations. However, this is the first work to implement a-state-of-the-art Catboost-based t-distributed stochastic neighbor embedding (t-SNE) approach to predict BB. A total of 62 datasets having 12 influential BB-generating features are collected from genuine blasting patterns. A novel dimensionality depletion technique t-SNE that operates the Kullback-Leibler divergence interpretation is employed to tailor the pioneer exaggeration of the blasting dataset. Then the t-SNE dataset obtained is split into a 70:30 ratio of the training and testing datasets. Finally, the Catboost method is implemented on a low-dimensionality blasting database. The performance evaluation criterion confirms that the BB predictive model is more stable with a goodness of fit = 99.04 in the training dataset, 97.26 in the testing datasets, and could anticipate a more accurate prediction. Moreover, the model presented in this work performs superior to the existing publicly available execution of BB. In summary, this model can be practiced in order to predict BB in several rock engineering practices and mining industry scenarios. | ||
| کلیدواژهها | ||
| blasting؛ backbreak؛ Catboost؛ rock engineering؛ mining industry | ||
| مراجع | ||
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[1]. Bhandari, S. (1997). Engineering rock blasting operations.
[2]. Raina AK, Murthy VMSR, Soni AK. (2014) Fly-rock in bench blasting: a comprehensive review. Bull Eng Geol Environ. doi:10.1007/s10064-014-0588-6.
[3]. Raina, A.K., Haldar, A., Chakraborty, A.K., Choudhury, P.B., Ramulu, M., and Bandyopadhyay, C. (2004). Human response to blast-induced vibration and air-overpressure: an Indian scenario. Bulletin of Engineering Geology and the Environment. 63 (3): 209-214.
[4]. Marto, A., Hajihassani, M., Jahed Armaghani, D., Tonnizam Mohamad, E., and Makhtar, A.M. (2014). A novel approach for blast-induced fly-rock prediction based on imperialist competitive algorithm and artificial neural network. The Scientific World Journal, 2014.
[5]. Hajihassani, M., Armaghani, D.J., Monjezi, M., Mohamad, E.T., and Marto, A. (2015). Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach. Environmental Earth Sciences. 74 (4): 2799-2817.
[6]. Armaghani, D.J., Mohamad, E.T., Hajihassani, M., Abad, S.A.N.K., Marto, A., and Moghaddam, M.R. (2016). Evaluation and prediction of fly-rock resulting from blasting operations using empirical and computational methods. Engineering with Computers. 32 (1): 109-121.
[7]. Rustan, A. (1998). Rock blasting terms and symbols.
[8]. Wyllie D.C., Mah C., 2004. Rock Slope Engineering, Fourth Edition: Fourth edition. Taylor and Francis. Retrieved from http://books.google.com.pk/books?id=4Gd7Hg2tz-sC.
[9]. Workman L., 1992. Wall Control. Retrieved from http://intrawww.ing.puc.cl/siding/public/ingcursos/cursos_pub/descarga.phtml?id_curso_ic=1781&id_archivo=69274.
[10]. Monjezi M. and Dehghani H., 2008. Evaluation of effect of blasting pattern parameters on back-break using neural networks. International Journal of Rock Mechanics and Mining Sciences. 45 (8):1446-1453. http://doi.org/10.1016/j. ijrmms.2008.02.007.
[11]. Muhammad, K. and Shah, A. (2017). Minimizing back-break at the Dewan Cement limestone quarry using an artificial neural network. Archives of Mining Sciences. 62 (4).
[12]. Kamali, M. and 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.
[13]. Ghasemi, E., Sari, M., and Ataei, M. (2012). Development of an empirical model for predicting the effects of controllable blasting parameters on fly-rock distance in surface mines. International Journal of Rock Mechanics and Mining Sciences. 52: 163-170.
[14]. Ghasemi, E., Ataei, M., and 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.
[15]. Taji, M., Ataei, M., Goshtasbi, K., and Osanloo, M. (2013). ODM: a new approach for open-pit mine blasting evaluation. Journal of vibration and control. 19 (11): 1738-1752.
[16]. Ataei, M. and 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.
[17]. Ghasemi, E., Amini, H., Ataei, M., and Khalokakaei, R. (2014). Application of artificial intelligence techniques for predicting the fly-rock distance caused by blasting operation. Arabian Journal of Geosciences. 7 (1): 193-202.
[18]. Sari, M., Ghasemi, E., and Ataei, M. (2014). Stochastic modeling approach for the evaluation of back-break due to blasting operations in open pit mines. Rock Mechanics and Rock Engineering. 47 (2): 771-783.
[19]. Ataei, M. and Sereshki, F. (2017). Improved prediction of blast-induced vibrations in limestone mines using Genetic Algorithm. Journal of Mining and Environment. 8 (2): 291-304.
[20]. Hoseini, S.M., Sereshki, F., and Ataei, M. (2018). A quantitative model for evaluation and classification of blastings in open-pit mines. Journal of Mining and Environment. 9 (1): 127-141.
[21]. Mottahedi, A., Sereshki, F., and Ataei, M. (2018). Development of over-break prediction models in drill and blast tunneling using soft computing methods. Engineering with computers. 34 (1): 45-58.
[22]. Khandelwal, M. and Singh, T.N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. Journal of sound and vibration. 289 (4-5): 711-725.
[23]. Rezaei, M., Monjezi, M., and Varjani, A.Y. (2011). Development of a fuzzy model to predict fly-rock in surface mining. Safety science. 49 (2): 298-305.
[24]. Lundborg, N. (1974). The hazards of fly-rock in rock blasting. Swedish Detonic Research Foundation, Reports DS, 12.
[25]. Hajihassani, M., Armaghani, D.J., Sohaei, H., Mohamad, E.T., and Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics. 80: 57-67.
[26]. Faradonbeh, R.S., Monjezi, M., and Armaghani, D.J. (2016). Genetic programing and non-linear multiple regression techniques to predict back-break in blasting operation. Engineering with computers. 32 (1): 123-133.
[27]. Ebrahimi, E., Monjezi, M., Khalesi, M.R., and Armaghani, D.J. (2016). Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm. Bulletin of Engineering Geology and the Environment. 75 (1): 27-36.
[28]. Monjezi, M., Ahmadi, Z., Varjani, A.Y., and Khandelwal, M. (2013). Back-break prediction in the Chadormalu iron mine using artificial neural network. Neural Computing and Applications. 23 (3): 1101-1107.
[29]. 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 back-break. Journal of Rock Mechanics and Geotechnical Engineering. 5 (4): 318-324.
[30]. Mohammadnejad, M., Gholami, R., Sereshki, F., and Jamshidi, A. (2013). A new methodology to predict backbreak in blasting operation. International journal of rock mechanics and mining sciences. (1997). 60: 75-81.
[31]. Esmaeili, M., Osanloo, M., Rashidinejad, F., Bazzazi, A.A., and Taji, M. (2014). Multiple regression, ANN and ANFIS models for prediction of back-break in the open-pit blasting. Engineering with computers. 30 (4): 549-558.
[32]. Monjezi, M., Bahrami, A., Varjani, A.Y., and Sayadi, A.R. (2011). Prediction and controlling of fly-rock in blasting operation using artificial neural network. Arabian Journal of Geosciences. 4 (3-4): 421-425.
[33]. Monjezi, M., Khoshalan, H.A., and Varjani, A.Y. (2012). Prediction of fly-rock and back-break in open-pit blasting operation: a neuro-genetic approach. Arabian Journal of Geosciences. 5 (3): 441-448.
[34]. Monjezi, M., Bahrami, A., and Yazdian Varjani, A. (2010). Simultaneous prediction of fragmentation and fly-rock in blasting operation using artificial neural networks. International journal of rock mechanics and mining sciences (1997). 47 (3): 476-480.
[35]. Monjezi, M. and Dehghani, H. (2008). Evaluation of effect of blasting pattern parameters on back-break using neural networks. International Journal of Rock Mechanics and Mining Sciences. 45 (8): 1446-1453.
[36]. Belkina, A.C., Ciccolella, C.O., Anno, R., Halpert, R., Spidlen, J., and Snyder-Cappione, J.E. (2019). Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature communications. 10 (1): 1-12.
[37]. Zhu, W., Webb, Z. T., Mao, K., and Romagnoli, J. (2019). A deep learning approach for process data visualization using t-distributed stochastic neighbor embedding. Industrial and Engineering Chemistry Research. 58 (22): 9564-9575.
[38]. Hinton, G. and Roweis, S.T. (2002, December). Stochastic neighbor embedding. In NIPS (Vol. 15, pp. 833-840).
[39]. Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research. 9 (11).
[40]. Rogovschi, N., Kitazono, J., Grozavu, N., Omori, T., and Ozawa, S. (2017, May). t-Distributed stochastic neighbor embedding spectral clustering. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 1628-1632). IEEE.
[41]. Weinberger, K.Q., Sha, F., and Saul, L.K. (2004, July). Learning a kernel matrix for non-linear dimensionality reduction. In Proceedings of the twenty-first international conference on Machine learning (p. 106).
[42]. Tao, K., Cao, J., Wang, Y., Mi, J., Ma, W., and Shi, C. (2020). Chemometric Classification of Crude Oils in Complex Petroleum Systems using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm. Energy and Fuels, 34(5), 5884-5899.
[43]. Liu, H., Yang, J., Ye, M., James, S.C., Tang, Z., Dong, J., and Xing, T. (2021). Using t-distributed Stochastic Neighbor Embedding (t-SNE) for cluster analysis and spatial zone delineation of groundwater geochemistry data. Journal of Hydrology, 597, 126146.
[44]. Arnø, M.L., Godhavn, J.M., and Aamo, O.M. (2021). At-bit estimation of rock density from real-time drilling data using deep learning with online calibration. Journal of Petroleum Science and Engineering, 109006.
[45]. Dorogush, A.V., Ershov, V., and Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.
[46]. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2017). CatBoost: unbiased boosting with categorical features. arXiv preprint arXiv:1706.09516.
[47]. Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., ... and Zhou, H. (2019). Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions. Journal of Hydrology, 574, 1029-1041.
[48]. Hancock, J. and Khoshgoftaar, T.M. (2020, August). Medicare fraud detection using Catboost. In 2020 IEEE 21st international conference on information reuse and integration for data science (IRI) (pp. 97-103). IEEE.
[49]. Zhong, C., Geng, F., Zhang, X., Zhang, Z., Wu, Z., and Jiang, Y. (2021, May). Shear Wave Velocity Prediction of Carbonate Reservoirs Based on CatBoost. In 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD) (pp. 622-626). IEEE.
[50]. Cardoso, A., Leitão, J., and Teixeira, C. (2018, September). Using the Jupyter notebook as a tool to support the teaching and learning processes in engineering courses. In International Conference on Interactive Collaborative Learning (pp. 227-236). Springer, Cham.
[51]. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. The Journal of machine Learning research, 12, 2825-2830.
[52] Althnian, A., AlSaeed, D., Al-Baity, H., Samha, A., Dris, A.B., Alzakari, N. and Kurdi, H. (2021). Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain. Applied Sciences. 11 (2): 796.
[53]. Willmott, C.J. (1982). Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society. 63 (11): 1309-1313.
[54]. Kamran, M. (2021). A Probabilistic Approach for Prediction of Drilling Rate Index using Ensemble Learning Technique. Journal of Mining and Environment.
[55]. Kamran, M., Bacha, S., and Mohammad, N. (2021). A Stochastic Model Updating Gold Reserve Estimation by Using Monte Carlo Simulation. Journal of Mining and Environment.
[56]. Guo, H., Nguyen, H., Bui, X.N., and Armaghani, D.J. (2021). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers. 37 (1): 421-435. | ||
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