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1 2025

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Prediction Models with Multiple Linear Regressionfor Improving Acoustic Performance of Textile Industry Plants

Yaman Muammer, Kurtay Cüneyt, Ulukavak- Harputlugil Gülsu

Archives of Acoustics

2025

Vol. 50, nr 1

3--16

In industrial plants noise is a major threat to the mental and physical health of employees. The risk increasesmore due to the presence of high noise sources and the presence of too many employees in textile industryplants. This paper aims to predict the consequences of variables that may arise in the plants for acousticimprovement in textile industry plants. For this purpose, scenario plants have been created according to archi-tectural properties and source-transmission path-receiver characteristics. The acoustic analyses of the scenarioplants were performed in the ODEON Auditorium, andA-weighted sound pressure level (LA), noise reduc-tion (NR), and reverberation time (RT) were determined. From the data, prediction equations were createdwith a multiple linear regression (MLR) model. To test the prediction equations, acoustic measurements weremade, and acoustics improvements were carried out at a textile industry plant located in T ̈urkiye. When theobtained results, the success, validity, and reliability of the prediction method are provided. In conclusion,the effect of architectural properties and the surface absorption on acoustic improvements in the textile in-dustry was revealed. It was emphasized that prediction methods can be used to determine the effectiveness ofinterventions that can be applied in different facilities and can be improved in future studies.