Abstract
In an opencast mine, several hundred tonnes are regularly carried by haul roads. Thus, mine haul road plays an important role for the transportation. Consequently, at every stage of the mining process, prediction of the performance of haul road is utmost essential. The mine haul road performance is analyzed during designing of a road for new mines and extending or maintenance of an existing haul road. Speed of vehicle movement, fuel cost of the truck and dust emission rate are the three attributes that define the performance of a haul road. This paper shows a novel ANN regression model to divine the values of these three attributes. The proposed ANN regression model is designed with eight input variables/attributes. The paper also presents the theoretical and statistical justifications of choosing these eight input variables. Extensive experiments are carried out by collecting the data from two study areas. The proposed regression model is compared with linear and quadratic regression approaches. The tenfold cross-validation results show the superiority of ANN model over competing models.
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References
S. Yadav, Singh Kumar (Ali, Design of Surface Mine Haul Road, 2016)
R. Thompson, A. Visser, Selection and maintenance of mine haul road wearing course materials. Mining Technol. 115(4), 140–153 (2006)
D. Tannant, B. Regensburg, Guidelines for mine haul road design (2001)
A.J. Kean, R.F. Sawyer, R.A. Harley, A fuel-based assessment of off-road diesel engine emissions. J. Air Waste Manag. Assoc. 50(11), 1929–1939 (2000)
S. Sundin, C. Braban-Ledoux, Artificial intelligence-based decision support technologies in pavement management. Comput.-Aided Civil Infrastr. Eng. 16(2), 143–157 (2001)
B. Lal, S.S. Tripathy, Prediction of dust concentration in open cast coal mine using artificial neural network. Atmos. Pollut. Res. 3(2), 211–218 (2012)
E. Siami-Irdemoosa, S.R. Dindarloo, Prediction of fuel consumption of mining dump trucks: a neural networks approach. Appl. Energy 151, 77–84 (2015)
W. Hustrulid, M. Kuchta, R. Martin, Open Pit Mine Planning and Design, Volume 1: Fundamentals, vol. 6000 (CRC Press Taylor & Francis Group, 2006), pp. 33487–2742
S. Sinha, S. Banerjee, Characterization of haul road dust in an Indian opencast iron ore mine. Atmos. Environ. 31(17), 2809–2814 (1997)
R. Thompson, A. Visser, An overview of the structural design of mine haulage roads. J. South Afr. Inst. Min. Metall. 96(1), 29–37 (1995)
R. Thompson, A. Visser, R. Miller, T. Lowe, Development of real-time mine road maintenance management system using haul truck and road vibration signature analysis. Transp. Res. Rec. 1819(1), 305–312 (2003)
D. Hugo, et al., Haul road defect identification and condition assessment using measured truck response. PhD thesis, University of Pretoria (2005)
O. Williamson, Haul road design for off highway mining equipment subject to very high wheel loads, in Australian Road Research Board (ARRB) Conference, 13th, 1986, Adelaide. Australia vol. 13 (1986)
P. Holman, I. St Charles, Caterpillar® Haul Road Design and Management (Big Iron University, St Charles, IL, 2006)
A. Soofastaei, Development of an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines (2016)
K.A. Kubler, Optimisation of off-highway truck fuel consumption through mine haul road design (2015)
S. Chaulya, M. Ahmad, R. Singh, L. Bandopadhyay, C. Bondyopadhay, G. Mondal, Validation of two air quality models for Indian mining conditions. Environ. Monit. Assess. 82(1), 23–43 (2003)
J. Baek, Y. Choi, A new method for haul road design in open-pit mines to support efficient truck haulage operations. Appl. Sci. 7(7), 747 (2017)
SP30 I, Manual on economic evaluation of highway projects in india. Indian Roads Congress (2009)
H. Pankrath, M. Barthel, A. Knut, M. Bracciale, R. Thiele, Dynamic soil compaction-recent methods and research tools for innovative heavy equipment approaches. Procedia Eng. 125, 390–396 (2015)
W.W. Kaufman, et al., Design of surface mine haulage roads: a manual (1978)
J.C. Pais, S.I. Amorim, M.J. Minhoto, Impact of traffic overload on road pavement performance. J. Transp. Eng. 139(9), 873–879 (2013)
R. Blab, J. Litzka, Measurements of the lateral distribution of heavy vehicles and its effects on the design of road pavements, in Proceedings of the International Symposium on Heavy Vehicle Weights and Dimensions (University of Michigan, Road Transport Technology, 1995), pp. 389–395
S. Zaghloul, T. White, Guidelines for permitting overloads-part i: effect of overloaded vehicles on the indiana highway network (1994)
A. Highway, T. Officials, Aashto guide for design of pavement structures 1993 (1993)
Ö. Kişi, Streamflow forecasting using different artificial neural network algorithms. J. Hydrol. Eng. 12(5), 532–539 (2007)
M. Rezaeianzadeh, H. Tabari, A.A. Yazdi, S. Isik, L. Kalin, Flood flow forecasting using ann, anfis and regression models. Neural Comput. Appl. 25(1), 25–37 (2014)
C.W. Dawson, R. Wilby, An artificial neural network approach to rainfall-runoff modelling. Hydrol. Sci. J. 43(1), 47–66 (1998)
B. Santra, A. Paul, D.P. Mukherjee, Deterministic dropout for deep neural networks using composite random forest. Pattern Recogn. Lett. 131, 205–212 (2020)
H.R. Maier, G.C. Dandy, The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ. Modell. Softw. 13(2), 193–209 (1998)
H. Yonaba, F. Anctil, V. Fortin, Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting. J. Hydrol. Eng. 15(4), 275–283 (2010)
M.R. Zadeh, S. Amin, D. Khalili, V.P. Singh, Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour. Manage 24(11), 2673–2688 (2010)
H. Yu, B.M. Wilamowski, Levenberg–marquardt training. Ind. Electron. Handbook 5(12), 1 (2011)
D.E. Rumelhart, G.E. Hinton, R.J. Williams et al., Learning representations by back-propagating errors. Cognit. Model. 5(3), 1 (1988)
J. O’Hagan, B. McCabe, Tests for the severity of multicolinearity in regression analysis: a comment. Rev. Econ. Stat. 368–370 (1975)
F.J. Lin, Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual. Quant. 42(3), 417–426 (2008)
N.R. Draper, H. Smith, Applied Regression Analysis, vol. 326 (Wiley, New York, 1998)
L. Breiman, Random forests Random forests. Mach. Learn. 45(1), 5–32 (2001)
B. Santra, D.P. Mukherjee, D. Chakrabarti, A non-invasive approach for estimation of hemoglobin analyzing blood flow in palm, in IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE vol. 2017, pp. 1100–1103 (2017)
F. Mosteller, J.W. Tukey, Data Analysis, Including Statistics, in Handbook of Social Psychology, vol. 2 (Addison-Wesley, Cambridge, 1968)
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Chowdhury, T., Sinha, S. & Roy, S.K. Analysis of Mine Haul Road Performance Using Artificial Neural Network. J. Inst. Eng. India Ser. D 102, 7–18 (2021). https://doi.org/10.1007/s40033-021-00248-3
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DOI: https://doi.org/10.1007/s40033-021-00248-3