Noise Exposure Estimation of Surface-Mine- Heavy Equipment Operators Using Artificial Neural Networks

Ever inreasing demand to raw mineral production stimulates intense use of mining machinery and subsequentlyexposes mining machinery operators to high levels of continuous noise. Long-term exposure to high levels ofcontinuous noise can cause Occupational Hearing Loss (OHL) on operators. In order to certify a good workingenvironment, it is important to estimate real noise levels of opencast mining machines.The aim of this study was to assess exposure levels to continuous noise using the test records of continouos noiseemitted from mining machinery and recommend some actions to reduce it. Artificial neural networks (ANN) tooldeveloped by MATLAB software has been used for these estimates.During the study, consistent personal noise exposure levels emitting from 60 different opencast miningmachinery was recorded. The lowest, highest, average and equivalent noise levels of the machines wererecorded and possible exposure noise-levels (LEX,8H) on operators were calculated.Later, data obtained from tests were used to train the ANN multilayered model by forward-feed-fault-backcirculation algorithm. During modeling of ANN; vehicle types, recording times, ambient temperature andpressure and relative humudity were determined as input parameters. By the help of the model, equivalent andmomentary noise levels prior to maximum level were estimated. Following training and testing of the model, theobtained noise levels were examined by statistical analysis commonly used in ANN models. It was noticed thatthe designed model provided very close results to the actual test results and can be applied successfully.

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1. Utley, W. A. 1980. Noise from opencast coal mining sites. Applied Acoustics; vol. 13, no. 2, pp. 85–102.
2. Ghose, M.K. 1990. Noise pollution evaluation and its abatement measures in coal mining area. Minetech; vol. 11, no. 3, pp. 55–57.
3. Vardhan, H., Rao, Y. and Karmakar, N. 2004. Noise analysis of heavy earth moving machinery deployed in opencast mines and development of suitable maintenance guidelines for its attenuation - part 1. Noise and Vibration Worldwide; vol. 35, no. 8, pp. 11–24.
4. Sharma, O., Mohanan, V. and Singh, M. 1998. Noise emission levels in coal industry. Applied Acoustics, Elsevier; vol. 54, no. 1, pp. 1–7.
5. Barrientos, M.C., Lendrum, D.C. and Steenland, K. 2004. Occupational Noise; Assessing the Burden of Disease from WorkRelated Hearing Impairment at National and Local Levels. World Health Organization Protection of the Human Environment. Environmental Burden of Disease Series; No. 9. Geneva.
6. Donoghue AM. 2004. Occupational health hazards in mining: an overview. Occupational Medicine; 54: 283 - 289.
7. Ramlu, M.A. 2005. Occupational noise exposure and hearing damage. Mining Engineer’s Journal; vol. 6, no. 7, pp. 9–20.
8. Onder, S., Onder, M. 2018. Statistical investigation of the noise levels in coal mining industry. J. of Eng. & Arc. Fac. of Eskisehir Osmangazi Un.; 26 (1), 30-35.
9. Daniel, E. 2007. Noise and hearing loss: A review, Journal of School Health; 77, 5, 225-231.
10. Joy, G. J., Middendorf, P. J. 2007. Noise exposure and hearing conservation in us coal mines-a surveillance report. Journal of Occupational and Environmental Hygiene; vol. 4, no. 1, pp. 26–35,
11. Dekker JJ, Edwards AL, Franz RM, et al. 2011. Meeting the milestones: are South African small- to medium-scale mines up to the task. The Journal of The Southern African Institute of Mining and Metallurgy; 111: 309-313.
12. Edwards, A.L., Dekker, J.J., Franz, R.M., Dyk, T. V. and Banyini, A. 2011. Profiles of noise exposure levels in South African Mining. The Journal of The Southern African Institute of Mining and Metallurgy; vol. 111, pp. 315–322.
13. Çınar, İ., Şensöğüt, C. 2013. Evaluation of noise measurements performed in mining sites for environmental aspects. International Journal of Environmental Research; 7(2), 383-386.
14. Álvarez-Vigil, A.E., Gonzalez-Nicieza, C., Gayarre, F.L., Álvarez-Fernández, M.I. 2012. Predicting blasting propagation velocity and vibration frequency using artificial neural networks. International Journal of Rock Mechanics and Mining Sciences; 55, 108-116.
15. Görgülü, K., Arpaz, E. Demirci, A., Koçaslan, A., Dilmaç, M.K., Yüksek, A.G. 2013. Investigation of blast-induced ground vibrations in the Tülü boron open pit mine. Bulletin of Engineering Geology and the Environment; 72, 555-564.
16. Simangunsong, G.M., Wahyudi, S. 2015. Effect of bedding plane on prediction blast-induced ground vibration in open pit coal mines. International Journal of Rock Mechanics and Mining Sciences, 79, 1-8.
17. Nanda SK, Tripathy DP. 2011. Application of functional link artificial neural network for prediction of machinery noise in opencast mines. Advances in Fuzzy Systems, 4, 1-11.
18. Huang X. 2012. Study on ANN noise adaptability in application of industry process characteristics mining. Advanced Materials Research; 462:635-640.
19. Karacan, C.Ö. 2008. Modeling and prediction of ventilation methane emissions of US longwall mines using supervised artificial neural networks. International Journal of Coal Geology; 73, 371- 387.
20. Zhao, K., Chen, S. 2011. Study on artificial neural network method for ground subsidence prediction of metal mine. Procedia Earth and Planetary Science; 2, 177-182.
21. Naghadehi, M.Z., Jimenez, R., KhaloKakaie, R., Jalali, S.M.E. 2013. A new open-pit mine slope instability index defined using the improved rock engineering systems approach. International Journal of Rock Mechanics and Mining Sciences; 61, 1-14.
22. Siami-Irdemoosa, E., Dindarloo, S.R. 2015. Prediction of fuel consumption of mining dump trucks: A neural networks approach. Applied Energy; 151, 77-84.
23. Rahimdel MJ., Mirzaei M., Sattarvand J., Ghodrati B., Mirzaei Nasirabad H. 2017. Artificial neural network to predict the health risk caused by whole body vibration of mining trucks. Journal of Theor Appl Vibr Acous; 3(1), 1–14.
24. Nguyen H, Bui X-N, Bui H-B, Mai N-L. 2020. A comparative study of artificial neural networks in predicting blast-induced airblast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl; 32:3939–3955.
25. Nguyen, H., Bui, X.-N., Tran, Q.-H., Le, T.-Q., Do, N.-H., & Hoa, L. T. T. 2019. Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Applied Sciences; 1(1), 125.