Estimation of Pavement Macrotexture by Principal Component Analysis of Acoustic Measurements

AbstractA method is developed to estimate the mean texture depth (MTD) value, which to some extent represents pavement quality, using measurements from a microphone mounted underneath a moving vehicle. Such measurements will include tire-generated sound that carries information about the road macrotexture as well as noise generated by the wind and vehicle. The proposed method uses principal component analysis (PCA) to differentiate important information about the road surface from noisy data while the vehicle is moving. The variations in frequency of the noise are assumed to be small compared with the variations in frequency of the signal related to the road-surface condition, which allows the PCA approach to separate noise from signals that carry information about the road-surface condition. The analysis begins with acoustic pressure measurements being made over various road-surface conditions underneath a moving vehicle. Fourier transforms are taken over various time windows and a PCA is performed over ...

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