Asphalt pavement macrotexture monitoring in cracked surfaces by using an acoustical low-cost continuous method

Road surface friction is one of the most important parameters determining road safety and macrotexture is one of the important factors affecting it. There are two types of traditional and automated data gathering methods. One of the disadvantages of automated methods is the high cost of purchasing or manufacturing. The purpose of this study is evaluating the feasibility of a low-cost dynamic method based on tire/road noise to evaluate the macrotexture of cracked non-porous asphalt pavements. To this end, the tire/road noise gathering system has been developed inspired by the ISO 11819-2 standard. The developed system has been used for data collection alongside with the health monitoring system of the Amirkabir University of Technology. After data gathering, the relationship between macrotexture indices (laser based) and tire/road noise features has been evaluated. Using artificial neural networks, models for relating different sound and macrotexture indices in each frequency band are presented with the best correlation result being R-2 = 0.76 between MPD and power spectral density in the 500 to 1500 Hz frequency band. The results indicate that in cracked pavement surfaces, by focusing on the vibration mechanism, acceptable results can be achieved in macrotexture monitoring by using interaction noise.