Prediction of backbreak in open pit blasting by Adaptive Neuro-Fuzzy Inference System

• Autorzy: Bazzazi A. A. , Esmaeili M.

Warianty tytułu

PL

Prognozowanie spękań skał przy pracach strzałowych w kopalniach odkrywkowych przy użyciu metod neuronowych i wnioskowania rozmytego (ANFIS) zastosowanych w modelu adaptywnym

• Języki publikacji: EN

Abstrakty

EN

Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

PL

Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (ANFIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście - wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji (R2) obliczonego na podstawie pomiarów pęknięć skał i wartości uzyskanych z modelowania. Wartości wskaźników RMSE, VAF i R2 obliczonych przy użyciu modelu ANFIS wynoszą odpowiednio 0.6, 0.94 i 0.95. Wielkości te wyraźnie potwierdzają wysoką skuteczność modelu.

Słowa kluczowe

EN

blasting; backbreak; adaptive neuro-fuzzy inference system; Sangan iron mine

PL

prace strzałowe; pękanie skał; system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej; kopalnia rud żelaza Sangan

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2012
• Tom: Vol. 57, no. 4
• Strony: 933--943
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Bazzazi A. A.

• Deprartment of Mining Engineering, Savadkooh Branch, Islamic Azad University, Savadkooh, Iran

autor

Esmaeili M.

• Department of Mining Engineering, Science & Research Branch, Islamic Azad University, Tehran, Iran

Bibliografia

• Aghajani Bazzazi A., 2005: Application of controlled blasting (pre-splitting) using large diameter hole in Sarcheshmeh copper mine. MSc Thesis, Shahid Bahonar University, Kerman, Iran.
• Akcayol M.A., 2004: Application of adaptive neuro-fuzzy controller for SRM. Advances in Engineering Software 35: 129-137.
• Alvarez Grima M., Babuska R., 1999: Fuzzy model for the prediction of unconfined compressive strength of rock samples. Int. J. Rock Mech. Min. Sci., 36: 339-349.
• Ata R., Kocyigit Y., 2010: An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications 37: 5454-5460.
• Bakhtyar R., Yeganeh Bakhtiary A., Ghaheri A., 2008: Application of neuro-fuzzy approach in prediction of runup in swash zone. Applied Ocean Research 30: 17-27.
• Cakmakci M., 2007: Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge. Bioprocess Biosyst. Eng. 30: 349-357.
• Ciurana J., Quintana G., Garcia Romeu M.L., 2008: Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach. Int. J. Production Economics 115: 171-178.
• Esmaeili M., 2011: Prediction of backbreak causing by blasting in open pit mines using neural and fuzzy set theory: A case study on northern C anomaly of Sangan iron mine. MSc Thesis, Islamic Azad University, Science & Research Branch, Tehran, Iran.
• Esmaeili M., Aghajani Bazzazi A., Brona S., 2011: Reliability analysis of a fleet of loaders in Sangan iron mine. Arch. Min. Sci. 56(4): 629-640.
• Gate W.C., Ortiz B.L.T., Florez R.M., 2005: Analysis of rockfall and blasting backbreak problems. In: Paper ARMA/USRMS, Proceedings of the American rock mechanics conference 5: 671-680.
• Gokceoglu C., Yesilnacar E., Sonmez H., Kayabasi A., 2004: A neurofuzzy model for modulus of deformation of jointed rock masses. Computers and Geotechnics 31(5): 375-383.
• Grima M.A., Bruines P.A., Verhoef P.N.W., 2000: Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunn. Undergr. Space Technol. 15(3): 260-269.
• Guler I., Ubeyli E.D., 2004: Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Systems with Applications 27: 323-330.
• Iphar M., Yavuz M., Ak H., 2008: Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental Geology 56: 97-107.
• Jang J.S.R., 1993: ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23(5/6): 665-684.
• Jang J.S.R., Sun C.T., Mizutani E., 1997: Neuro-fuzzy and soft computing: A computational approach to learning and machine intelligence. Prentice-Hall, Englewood Cliffs.
• Khajeh A., Modarress H., Rezaee B., 2009: Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Systems with Applications 36: 5728-5732.
• Konya C.J., Walter E.J., 1991: Rock blasting and overbreak control. U.S Department of transportation Federal Highway Administration office of Implementation.
• Kucuk K., Aksoy C.O., Basarir H., Onargan T., Genis M., Ozacar V., 2011: Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modeling. Tunn. Undergr. Space Technol. 26(1): 38-45.
• Lotfi A.Z., 1995: Fuzzy Logic Toolbox for use with MATLAB. User’s Guide Handbook, Version 2, 38. The Math. Works Inc., Berkeley, California, pp: 109-112.
• Mohammadi S.S., Bakhshandeh Amnieh H., Bahadori M., 2011: Predicting ground vibration caused by blasting operation in Sarcheshmeh copper mine considering the charge type by adaptive neuro-fuzzy inference system (ANFIS). Arch. Min. Sci. 56(4): 701-710.
• Monjezi M., Dehghani H., 2008: Evaluation of effect of blasting pattern parameters on backbreak using neural networks. Int. J. Rock Mech. Min. Sci. 45: 1446-1453.
• Naadimuthu G., Liu D.M., Lee E.S., 2007: Application of an adaptive neural fuzzy inference system to thermal comfort and group technology problems. Computers and Mathematics with Applications 54: 1395-1402.
• Radulovic J., Rankovic V., 2010: Feed forward neural network and adaptive network-based fuzzy inference system in study of power lines. Expert Systems with Applications 37: 165-170.
• Sargolzaei J., Kianifar A., 2010: Neuro-fuzzy modeling tools for estimation of torque in Savonius rotor wind turbine. Advances in Engineering Software 41: 619-626.
• Singh T.N., Kanchan R., Verma A.K., Saigal K., 2005: A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass. J. Earth Syst. Sci. 114(1): 75-86.
• Sumathi S., Paneerselvam S., 2010: Computational intelligence paradigms: theory and applications using MATLAB. Taylor and Francis Group, LLC.
• Tayebi Khorami M., Chehreh Chelgani S., Hower J.C., Jorjani E., 2011: Studies of relationships between free swelling index (FSI) and coal quality by regression and adaptive neuro fuzzy inference system. International Journal of Coal Geology 85: 65-71.
• Tzamos S., Sofianos A.I., 2006: Extending the Q system’s prediction of support in tunnels employing fuzzy logic and extra parameters. Int. J. Rock Mech. Min. Sci. 43:938-949.
• Wang Y.M., Elhag T.M.S., 2008: An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Systems with Applications 34: 3099-3106.
• Yagiz S., Gokceoglu C., 2010: Application of fuzzy inference system and nonlinear regression models for predicting rock brittleness. Expert Syst. Appl. 37: 2265-2272.
• Yan H., Zou Z., Wang H., 2010: Adaptive neuro fuzzy inference system for classification of water quality status. J. Env. Sci. 22(12): 1891-1896.
• Yilmaz I., Kaynar O., 2011: Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications 38: 5958-5966.
• Yilmaz I., Yuksek G., 2009: Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int. J. Rock Mech. Min. Sci. 46(4): 803-810.
• Zaheeruddin, Garima, 2006: A neuro-fuzzy approach for prediction of human work efficiency in noisy environment. Applied soft computing 6: 283-294.