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

Abstract

Ever inreasing demand to raw mineral production stimulates intense use of mining machinery and subsequently exposes mining machinery operators to high levels of continuous noise. Long-term exposure to high levels of continuous noise can cause Occupational Hearing Loss (OHL) on operators. In order to certify a good working environment, 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 noise emitted from mining machinery and recommend some actions to reduce it. Artificial neural networks (ANN) tool developed by MATLAB software has been used for these estimates. During the study, consistent personal noise exposure levels emitting from 60 different opencast mining machinery was recorded. The lowest, highest, average and equivalent noise levels of the machines were recorded 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-back circulation algorithm. During modeling of ANN; vehicle types, recording times, ambient temperature and pressure and relative humudity were determined as input parameters. By the help of the model, equivalent and momentary noise levels prior to maximum level were estimated. Following training and testing of the model, the obtained noise levels were examined by statistical analysis commonly used in ANN models. It was noticed that the designed model provided very close results to the actual test results and can be applied successfully.

Keywords

• Noise exposure levels
• mining industry
• opencast mine vehicle operators
• artificial neural networks
• opencast mining

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