详情

Developing Standard Definitions for Comparable Pavement Cracking Data

项目名称：	Developing Standard Definitions for Comparable Pavement Cracking Data
摘要：	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. 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 is 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 that quality control measures and processing logic for automated data evaluation software is capable of identifying cracking features and excluding non-cracking features. Application to both existing and emerging image-based data collection technologies shall be considered.
状态：	Active
资金：	249952
资助组织：	Federal Highway Administration
项目负责人：	Harrigan, Edward
执行机构：	Oklahoma State University
主要研究人员：	Wang, Kelvin
开始时间：	20171013
预计完成日期：	20200831
实际结束时间：	0