ESTIMATING THE NOISE-INDUCED HEARING LOSSES UNDER FUZZY ENVIRONMENT
Abstract
Noise causes many negative effects both in our daily life and working life, reduces our quality of life, and affects our mental health directly or indirectly. The most common consequence of noise exposure is especially permanent hearing loss called noise-induced hearing loss (NIHL). NIHL is very prevalent in almost every stage of the mining industry. Therefore, the assessment of noise levels of mining operations and the estimation of NIHLs of employees is an important issue to prevent and minimize them. This study is aimed to the modeling of NIHL prediction at a quarry located in Aksaray, Turkey. Initially, noise levels were measured with a sound level meter for employees working in different positions for the quarry, and daily exposure levels (Lex,8h) were determined. Audiometry tests were also performed on all employees and NIHLs were evaluated and determined by an audiometrist. According to the results, 5 employees had NIHL in this enterprise. A fuzzy inference system (FIS)-based NIHL estimating model implemented on fuzzy logic using the Sugeno inference mechanism was developed. The model predicts NIHLs for given occupation, age, experience, and Lex,8h parameters. To determine the accurate prediction ability of the model, field noise measurements and audiometry test results data were used. The obtained results indicated that the model has accurate a prediction ability with a 94% success rate. This study proposes a method with high predictive ability using fuzzy sets theory, and will be a guide for the top management in considering the damage effects of noise in enterprises.
Keywords
Open-pit mining , Noise-induced hearing loss , Fuzzy logic , Fuzzy inference system
References
- Acaroglu, O., Ozdemir, L., & Asbury, B. (2008). A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn. Undergr. Space Technol. 23(5), 600-608. doi: https://doi.org/10.1016/j.tust.2007.11.003
- Chadambuka, A., Mususa, F., & Muteti, S. (2013). Prevalence of noise induced hearing loss among employees at a mining industry in Zimbabwe. Afr. Health Sci., 13(4) 899-906. doi: http://dx.doi.org/10.4314/ahs.v13i4.6
- Cinar, I., & Sensogut, C. (2009). Evaluation of Environmental Factors Affecting Noise Propagation. Environmental Monitoring and Assessment, 153, 377-382. doi: https://doi.org/10.1007/s10661-008-0364-9
- Cinar, I., & Sensogut, C. (2013). Evaluation of noise measurements performed in mining sites for environmental aspects. International Journal of Environmental Research, 7(2), 383-386. Retrieved from https://ijer.ut.ac.ir/article_616_a7e43c6ed488624957059e7839a7659c.pdf
- Çalış, S. (2022) Measurement in occupatıonal health and safety noise, Ankara: İKSAD Publishing House, ISBN: 978-625-8423-96-9. Retrieved from https://iksadyayinevi.com/wpcontent/uploads/2022/01/IS-SAGLIGI-VE-GUVENLIGINDE-OLCUM GURULTU.pdf.
- Danish, E., & Onder, M. (2020). Application of fuzzy logic for predicting of mine fire in underground coal mine. Saf. Health Work 11(3), 322-334. https://doi.org/10.1016/j.shaw.2020.06.005
- DIRECTIVE 89/391/EEC of 12 June 1989 on the introduction of measures to encourage improvements in the safety and health of workers at work [Internet]. Retrieved from https://oshwiki.eu/wiki/Noise#cite_note-Lit_31-
- DIRECTIVE 2003/10/EC of 6 February 2003 on the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents (noise) [Internet]. Retrieved from https://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2003:042:0038:0044:EN:PDF
- Dyussenbayev, A. (2017). Age periods of human life. Advances in Social Sciences Research Journal, 4(6). doi: https://doi.org/10.14738/assrj.46.2924
- Erol, İ. (2022a). Investigation of noise and vibration exposure of underground coal mining machinery operators. Cukurova University Journal of the Faculty of Engineering, 37(1), 55-65. doi: https://doi.org/10.21605/cukurovaumfd.1094945