Pavement macrotexture measurement using tire/road noise

Abstract

A method is developed to estimate pavement macrotexture depth (MTD), using measurements from a microphone mounted underneath a moving vehicle. The acoustic energy is assumed to have positive linear correlation with MTD of the pavement. However, the acoustic measurements will include tire-generated sound that carries information about the road features as well as noise generated by the environment and vehicle. The variations in frequency of the noise are assumed to be small compared to the variations in frequency of the signal related to road features, which allows principal component analysis (PCA) to filter noise from microphone data prior to estimating its energy over an optimally selected bandwidth. The acoustic energy computed from the first principal component (PC) is termed as PCA energy, which is an important variable for MTD prediction. The frequency band most relative to pavement macrotexture was determined to be 140–700 Hz. Then, an MTD prediction model was built based on a Taylor series expansion with two variables, PCA energy and driving speed. The model parameters were obtained from an engineered track (interstate highway) with known MTD and then applied to urban roads for the feasibility test. The predicted MTD extends its range from 0.4–1.5 mm of the engineered track to 0.2–3 mm, which is the typical range of MTD. In addition, the excellent repeatability of the MTD prediction is demonstrated by the urban road test. Moreover, the potential to use the predicted MTD for pavement condition assessment is discussed. Therefore, the PCA Energy Method is a reliable, efficient, and cost-effective approach to predict equivalent MTD for engineering applications as an important index for pavement condition assessment.