Use of Digital Image Modeling for Evaluation of Concrete Pavement Macrotexture and Wear

Modeling of the pavement image formation process by using reflection properties of macrotexture showed that digital images of concrete pavements can be used to monitor pavement wear. The specific optical characteristics of images and the optimum camera settings that can be used for this purpose were determined by theoretically formulating the Bidirectional Reflection Distribution Function (BRDF) of surface texture with uniform color. In the analytical phase of the study, desired levels of pavement texture were generated by combining a series of 3D sine surfaces of varying wavelengths and amplitudes. The optimum specular settings of the overhead point light source and the digital area-scan camera for effective highlighting of the imaged wheel path macrotexture were determined with an analytical formulation on the basis of a simplistic and physically meaningful BRDF model. It was also shown that the images obtained by the theoretical formulation closely resemble those captured from a similarly textured experimental surface under identical lighting and imaging conditions. In particular, the pavement image formation model revealed that quantifiable changes in the brightness of images do occur because of changes in texture depth and spacing (wavelength). In the next phase of the study, the traffic-induced pavement wearing process was simulated by gradual smoothening of the modeled surfaces, and then images corresponding to each wearing stage were generated. The theoretically predicted variation of the image brightness resulting from wear was experimentally verified by using images from a gradually worn-out concrete specimen. Finally, it was illustrated how the brightness evaluation of wheel path images has the potential to be a screening tool to monitor the degradation of macrotexture and, hence, the skid-resistance of pavements at the network-level. DOI: 10.1061/(ASCE)TE.1943-5436.0000347. (C) 2012 American Society of Civil Engineers.