Predicting and modeling the effects of turbines noise on operator’s mental task performance in AL-DORA power plant - Mosa, Ali I.; Al-Sadawi, Laith A.; Attia, Osam H.; Adam, Nor Mariah (Scientific Route, 2024)
Noise has become one of the most critical environmental challenges. As one of noise kinds, the discomfort level of environmental noise can affect both personal quality of life and hearing sensitivity. An example is workplace noise pollution, which affects employees’ regular functioning and profoundly impacts their mental, auditory health, and psychological well-being. In order to tackle these issues, the need for adaptive intelligent systems has significantly grown. This study aims to evolve a neuro-fuzzy model for predicting the effects of noise pollution on employee’s work efficiency as a function of noise level and exposure time at Al-DORA Power Plant in Baghdad city. Participants’ responses were used to develop a neural-fuzzy logic model based on artificial neural networks (ANN) and fuzzy inference systems (FIS). The model is performed using the fuzzy logic toolbox inherited from the MATLAB software. The measurements were carried out for duration of nine weeks, three times a day during summer, and the extensive noise level was up to 110 dB. Results in the trapezoidal-shaped membership form showed a discernible pattern or trend in the fluctuation of membership degree in relation to noise levels. The same trend could be seen for the exposure time. Furthermore, the results showed that the efficiency of the workers depends on the noise level and exposure duration. It has been confirmed that a medium noise level can influence workers’ performance over a medium exposure time to a certain degree. Moreover, low noise levels can still affect the performance of workers who are exposed to noise for long durations. With this clear relationship between noise levels, exposure time, and mental work efficiency, organizations can implement certain strategies to optimize their acoustic environments.