Automatically detect and classify asphalt pavement raveling severity using 3D technology and machine learning

Abstract

Raveling is one of the most common asphalt pavement distresses that occur on US highway pavements. Raveling results in safety concerns such as loose stones and hydroplaning; poor ride quality and road/tire noise; and shortened pavement longevity. Traditional raveling survey methods involve manual visual inspection, which is time consuming, subjective, and hazardous to highway workers. With the research project competitively selected and sponsored by the National Cooperative Highway Research Program (NCHRP) Innovation Deserving Exploratory Analysis (IDEA) program, the objective of this study is to develop an accurate raveling detection and classification algorithm using 3D pavement data that has become mainstream technologies for state Department of Transportations (DOTs) in the US for pavement condition evaluation, and to comprehensively validate these methods using large-scale, real-world data based on actual transportation agencies’ distress protocol (Severity levels 1, 2, and 3). A total of 65 miles of 3 D pavement data was collected on I-85 and I-285 in Georgia for training and testing. Three supervised machine learning techniques —AdaBoost with decision trees, support vector machine (SVM) and random forests—were developed for the detection and classification of raveling in the collected data. The random forest classifier had the b est performance, with precision values ranging from 75.6% for level 3 raveling to 97.6% for level 0 (no) raveling and recall values ranging from 86.9% for level 1 raveling to 96.1% for level 0 raveling on real world large-scale data. The developed raveling detection and severity level classification method has been successfully implemented to entire Georgia’s interstate highway system with1452.5 survey miles of asphalt pavements after the large-scale validation and refinement. The proposed method for raveling detection can be deployed to other transportation agencies for safer and more efficient assessment of roadway raveling conditions.