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Statistical analysis of acoustic measurements for assessing pavement surface condition

This work presents a method for assessing pavement surface condition using measurements from a microphone mounted underneath a moving vehicle. Such measurements will include tire-generated sound, which carries much information about the road condition, as well as noise generated by the wind and vehicle. The proposed method uses Principal Component Analysis (PCA) to extract the tire-generated sound from the noisy measurements. The analysis begins with acoustic pressure measurements made over constant and known road conditions. Fourier transforms are taken over various time windows and a PCA is performed over the resulting vectors, yielding to a set of principal component vectors for that road condition. Each road condition is characterized by a set of principal component vectors. These vector sets are used to analyze measurements from a road with unknown road conditions by finding the vector set that best represents the acoustic measurements from that road. Successful applications of this method are demonstrated by accurate estimations of the mean texture depth (MTD) of pavement directly from acoustic measurements.