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Quantification of Pavement Condition by Tire/Road Noise Measurement



A method is developed to assess the pavement condition, using measurements from a microphone mounted behind the tire of a moving vehicle. Such measurements will include tire/road noise that carries information about the road feature. The proposed method uses standard deviation (STD) of the amplitudes of frequency spectra from the acoustic measurement to assess the road condition while the vehicle is moving. During the driving, the change of road condition will excite the vehicle resonance varying with frequency, which will lead to the pressure varying with frequency. Hence the standard deviation of the frequency content of the tire/road noise can be used for pavement condition assessment. The analysis begins with acoustic pressure measurements being made over various road surface conditions. The road is divided into continuous sections of equal length. For each road section, Fourier Transforms are taken over various time windows and averaged and a standard deviation of the average frequency spectrum is computed. This yields a set of STDs representing the surface conditions of each road section. According to the amplitudes in the frequency spectra of the collected acoustic measurement the frequency range of concern is from DC to 2 kHz. A linear regression analysis is performed between the known pavement condition index (PCI) and the average STD of the road. The overall pavement condition is quantified by this resulting equation. Successful applications of this method are demonstrated by a high negative linear correlation with a coefficient of - 0.77 between the average STD for a specific road and its corresponding PCI. A linear regression equation is obtained to calculate PCI through the average STD for each road. The results indicate that the frequency spectrum of acoustic measurement could be used for pavement condition assessment through the statistical analysis in real time.


pavement condition assessment, tire/road noise, standard deviation, linear regression analysis, real timeText

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