Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique: Fid-Bau Portal

Mean Texture Depth Estimation of Exposed Aggregate Concrete Pavement Surface Texture Based on Photogrammetry Technique

Chhay, Lyhour / Kim, Jaehoon / Lee, Seung Woo

The pavement texture wavelength and mean texture depth (MTD) of the pavement macrotexture significantly affect functional performance. However, owing to its higher correlation with the wavelength, the Exposed Aggregate Concrete Pavement (EACP) texture was evaluated based on the MTD and exposed aggregate number (EAN) in the same location. The MTD contributes significantly to road surface friction and tire-pavement noise and is vital for anti-sliding and noise reduction. Conventional methods for MTD measurement require considerable human effort when the sample size is large and sensitive to the operator. Furthermore, it is time-consuming to measure the MTD together with the EAN. Recently, image-based estimation has become a new trend, owing to its economy and convenience. Therefore, this study aimed to estimate the MTD of the EACP pavement surface texture at an image-based level as an alternative measurement approach simultaneously within the EAN location. Initially, the image acquisition was created based on aerial photography. Subsequently, photogrammetry was used to reconstruct a high-resolution point cloud of the pavement texture. Subsequently, the MTD was estimated analytically from image-based point clouds. Experiments were conducted at over 60 locations in three field tests of the EACP in South Korea. The MTD results showed good agreement and a higher correlation between the image-based and sand-patch test (SPT) methods. The image-based method showed results higher than the SPT with a value of 22%. Therefore, the developed method can be used to estimate the MTD using the established regression equation.