Three-dimensional pavement surface texture measurement and statistical analysis
Pavement texture has been measured predominantly by using two-dimensional (2D) profile methods. The 2D profile based mean profile depth (MPD) is still the well accepted texture index which has been found inadequate to characterize pavement texture especially when tire/pavement friction and noise are involved. There is a lack of standard 3D texture indices which show strong correlation with pavement friction and noise. There is a need to use 3D texture measurement for more comprehensive understanding of texture. The objectives of this thesis are to characterize pavement surfaces using 3D texture parameters based on 3D texture measurement and to explore the relationship between 3D texture parameters, pavement friction, and pavement noise. Field tests are conducted at various pavement sections for the measurements of texture, friction, and noise. The tested pavements include Interstate highway, MnROAD test facilities, airport runway, and municipal streets. The findings and contributions of this thesis are: • The pavement surface texture is measured in a 3D manner by using a line-laser scanner with both horizontal sample interval and vertical accuracy better than 0.05 mm. • A 3D texture analysis procedure with discrete wavelet transform (DWT) is proposed to separate macrotexture from microtexture and to define texture indices independently. • 3D parameters for macrotextures and microtexture are proposed and verified by field tests. • The relationship between 3D and 2D macrotexture indices [i.e. SMTD and MPD; Sq and root mean square roughness (RMSR)] are established, which is useful for the purposes of data comparison between 3D and 2D methods. • The relationship is investigated between 3D macrotexture parameters (SMTD and Sq) and pavement friction and noise. • It is found that texture distribution indices (i.e. Ssk and Sku) are significant contributors to pavement friction and noise. The new 3D texture analysis procedure and texture indices proposed in this thesis can be used to characterize various pavement textures (concrete pavement, asphalt pavement, and pavement contains recycled materials) in 3D manner, to compare 3D with 2D texture measurement/indices for quality control purposes, and to evaluate and predict pavement friction and noise.
Pavement surface texture, 3D texture measurement, Discrete wavelet transform, 3D texture parameters, Statistical analysis