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This study investigates the influence of different surface characteristics on tire–road noise of thin-layer road surfacings by a statistical modeling method. The surface characteristics considered are surface texture and sound absorption. Correlation analysis and multivariate linear regression are performed to detect the combined effect of surface texture and sound absorption on tire–road noise levels. The texture is characterized by mean profile depth as well as texture level on an octave band of wavelengths, while the sound absorption is described by the absorption coefficients on the frequency spectrum as well as by the maximum absorption coefficient only. In the regression, principal component regression is used to reduce the multicollinearity between the explanatory variables. The influence of surface characteristics of thin layer surfacing on tire–road noise is observed from the regression results and the correlation analysis
This study investigates the influence of different surface characteristics on tire–road noise of thin-layer road surfacings by a statistical modeling method. The surface characteristics considered are surface texture and sound absorption. Correlation analysis and multi-variate linear regression are performed to detect the combined effect of surface texture and sound absorption on tire–road noise levels. The texture is characterized by mean profile depth as well as texture level on an octave band of wavelengths, while the sound absorption is described by the absorption coefficients on the frequency spectrum as well as by the maximum absorption coefficient only. In the regression, principal component regression is used to reduce the multicollinearity between the explanatory variables. The influence of surface characteristics of thin layer surfacing on tire–road noise is observed from the regression results and the correlation analysis