Many state and local agencies collect downward pavement imagery using highway-speed data collection vehicles. The images are subsequently processed using proprietary semi- or fully-automated crack detection and classification software to identify pavement cracking for use in asset management systems. There are multiple methods and software for defining, classifying, and reporting cracking data. In addition, these methods and the cracking data they produce are not always comparable between states, even if similar data collection and detection technologies are used. One outcome of this situation is that vendors must customize the cracking definitions for each client they serve. In order to unify data reporting, sharing, and evaluation, standardization of pavement cracking definitions is needed. Research is needed to define cracking measurement terms for uniformity and potential standardization, building upon work done in AASHTO PP 67 and 68. Additionally, research is needed to produce user and system requirements to aid in the future development of production-grade evaluation software for classifying cracking type, extent, and severity. The standard definitions will aid in sharing information among agencies and vendors as well as reporting to FHWA and setting national, state, and local performance goals.
The objective of this research was to develop standard, discrete definitions for common cracking types in flexible, rigid, and composite pavements. The definitions shall classify cracking type, extent, and severity based on information from images collected by highway-speed data collection vehicles, including orientation, length, density, displacement, location, and other relevant factors. The standard definitions shall be used to facilitate comparable measurement and interpretation of pavement cracking in the highway community. The definitions shall be of sufficient detail to serve as the basis for user and system requirements for cracking evaluation software for automated data collection. Application to both existing and emerging image-based data collection technologies shall be considered.