A fuzzy logic-based model for noise control at industrial workplaces

• Published in: Applied ergonomics Vol. 39; no. 3; pp. 368 - 378
• Main Authors: Aluclu, I., Dalgic, A., Toprak, Z.F.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2008; Elsevier; Elsevier Science Ltd
• Subjects: Applied physiology; Biological and medical sciences; Ergonomics; Ergonomics. Work place. Occupational physiology; Fuzzy Logic; Human factors research; Human physiology applied to population studies and life conditions. Human ecophysiology; Humans; Industrial plants; Industry; Medical sciences; Models, Organizational; Noise control; Noise, Occupational - prevention & control; Turkey; Work environment; Workplace; Turkey; Fuzzy logic; Ergonomics; Noise control; Human; Workplace layout; Work place; Noise; Noise reduction; Mathematical model
• ISSN: 0003-6870; 1872-9126
• DOI: 10.1016/j.apergo.2007.08.005

Ergonomics is a broad science encompassing the wide variety of working conditions that can affect worker comfort and health, including factors such as lighting, noise, temperature, vibration, workstation design, tool design, machine design, etc. This paper describes noise–human response and a fuzzy logic model developed by comprehensive field studies on noise measurements (including atmospheric parameters) and control measures. The model has two subsystems constructed on noise reduction quantity in dB. The first subsystem of the fuzzy model depending on 549 linguistic rules comprises acoustical features of all materials used in any workplace. Totally 984 patterns were used, 503 patterns for model development and the rest 481 patterns for testing the model. The second subsystem deals with atmospheric parameter interactions with noise and has 52 linguistic rules. Similarly, 94 field patterns were obtained; 68 patterns were used for training stage of the model and the rest 26 patterns for testing the model. These rules were determined by taking into consideration formal standards, experiences of specialists and the measurements patterns. The results of the model were compared with various statistics (correlation coefficients, max–min, standard deviation, average and coefficient of skewness) and error modes (root mean square error and relative error). The correlation coefficients were significantly high, error modes were quite low and the other statistics were very close to the data. This statement indicates the validity of the model. Therefore, the model can be used for noise control in any workplace and helpful to the designer in planning stage of a workplace.