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Title
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Noise Prediction in Industrial Work rooms Using Regression Modeling Methods Based on the Dominant Frequency Cutoff Point
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Type
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JournalPaper
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Keywords
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Multiple regression·Noise prediction·Modeling·Dominant frequency·Industrial workroom
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Abstract
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Noise pollution is one of the major problems inindustrial environments. The physiological response to the noise in industrial environments depends on the characteristics of the noise and environment. This study aimed to develop an empirical model forpredictingthelevelofnoiseinclosedindustrialspacesusingregressionmodelingbasedonthedominantfrequencycutoff point. After identifying and determining the effective input variables in the prediction of noise level, the relevant data were collected from 56 industrial workrooms and the model was developed using multiple regression technique. The two models were best fitted to estimate the noise level for workrooms with a dominant frequency of less or equal to and more than 250 Hz (R2 0.86, R2 0.85, respectively). Based on the results of this study, it is less costly and requires less equipment for noise evaluation and monitoring by the mentioned models during the design, implementation, and operation of industrial environments.Theseexperimentalmodelscanbeusedassuitablemeasuresforscreeningclosedindustrialspacesandranking them in terms of the amount of noise pollution.
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Researchers
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Hassan Khotanlou (Fifth Researcher), mohsen Aliabadi (Fourth Researcher), Alireza Soltanian (Third Researcher), Vahideh Abolhasannejad (Second Researcher), Rostam Golmohammadi (First Researcher)
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