DEVELOPMENT OF AN EMPIRICAL MODEL FOR NOISE PREDICTION IN TYPICAL INDUSTRIAL WORKROOMS BASED ON ARTIFICIAL NEURAL NETWORKS
Development of an empirical model for noise prediction in typical industrial workrooms based on artificial neural networks Mohsen Aliabadia, Rostam Golmohammadib*, Muharram Mansoorizadehc, Hassan Khotanloud and Abdoreza Ohadie a Department of Occupational Hygiene, Faculty of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. Email:
[email protected] b Department of Occupational Hygiene, Faculty of Public Health and Center for Health Researches, Hamadan University of Medical Sciences, Hamadan,Iran. Email:
[email protected] cDepartment of Computer Engineering, Faculty of Engineering, Buali Sina University, Hamadan, Iran. Email:
[email protected] dDepartment of Computer Engineering, Faculty of Engineering, Buali Sina University,Hamadan ,Iran. Email:
[email protected] eDepartment of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran. Email:
[email protected]
Abstract Noise prediction techniques can be employed as practical tools for evaluating the cost-effectiveness of acoustic treatments and consequently, prevent blind treatments by experts so that more acceptable conditions are obtained. One of the most important issues in this regard is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, artificial neural networks were employed to develop a relatively accurate model for noise prediction in noisy industrial workrooms. The data from nine acoustic, structural and embroidery process features influencing the noise in 60 embroidery workrooms was used to develop the noise prediction techniques. Multilayer feed forward neural networks with different structures were developed by using MATLAB. The best neural networks could accurately predict the noise level (RMSE=0.69 dB and R2=0.88). Although networks are empirical in nature, the results confirmed the potential of this approach for minimizing the uncertainties in acoustics modeling. This model gives professionals the opportunity to make an optimum decision about the effectiveness of acoustic treatment scenarios in workrooms. Keywords: Noise prediction, Neural networks, Industrial workroom, Empirical model
1. Introduction Noise is considered to be the most persistent physical contaminant in industrial workplaces for workers [1]. In the developing countries where, compared with developed countries, modern technology of designing, implementing and utilizing industrial processes is not easily accessible, industrial noise pollution is of great importance [2,3]. Long exposure to excessive noise can cause permanent hearing loss, which
finally results in the disturbance in verbal communication and in the quality of social behaviors [4]. In addition, exposure to high noise level contributes to undesirable physiologic effects such as hypertension and mental disorders such as discomfort and noise annoyance [5,6]. These health effects, which are all caused by the exposure to, noise can affect job performance and the productivity of workers [7]. Regarding the legal responsibility
Correspondence to: Mohsen Aliabadi, Department of Occupational Hygiene, Faculty of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. P.O. Box 4171-65175 Tel: +98(811)8380090 Fax: +98(811)8380509 Email:
[email protected] Email:
[email protected]
NOVEMBER 2013
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