New Regression Models for Predicting Noise Exposure in the Driver's Compartment of Malaysian Army Three-Tonne Trucks

摘要

The objective of this study is to present a new method for determination of noise exposure in the driver's compartment of Malaysian Army (MA) three-tonne trucks based on changing vehicle speed using regression models and the statistical analysis method known as Integrated Kurtosis-based Algorithm for Z-notch filter (I-kaz). The test was conducted on two different road conditions: tarmac and dirt roads. Noise exposure was measured using a sound level meter which is capable of recording raw sound pressure in Pa, and comparisons were made between the two types of roads. The prediction of noise exposure was done using the developed regression models and 3D graphic representations of the I-kaz coefficient Z(infinity). The results of the regression models show that Z(infinity) increases when vehicle speed and noise exposure increase. For model validation, predicted and measured noise exposures were compared, and a relatively good agreement has been obtained between them. It was found that the predictions had high accuracies and low average relative errors. By using the regression models, we can easily predict noise exposure inside the truck driver's compartment. The proposed models are efficient and can be extended to the automotive industry for noise exposure monitoring.