A stereometric knowledge-based system for maintenance of street networks

The main objective of this work was to investigate the potential of integrating a stereometric vision system, i.e., using digital stereo images, and a knowledge-based system for flexible pavement distress classification. Classification process includes distress type, severity level, and options for repair. A hybrid stereo vision and knowledge-based system (called K-PAVER) was developed. The system extracts distress measurements using a PC-based stereo vision system. Geometric surface measurements such as point locations, distances, areas, volumes, and surface areas could also be computed. The knowledge-based system developed utilizes a set of if...then rules from the PAVER system (a pavement maintenance management system for roads and streets) and related literatures. New parameters, including shape parameters, orientation, and some geometrical measurements, were introduced to the system in order to facilitate the distress classification process. A criterion for maintenance priorities based on four parameters was developed. These parameters are pavement condition index, average daily traffic, location of distressed pavement, and street class. Surface measurements and automatic classification decision-making were validated and tested for all distress types. The developed system gives accurate results in both the measurement mode and the decision-making phase. This result opens the door for a fully automated distress classification process without any human intervention.