Abstract:
The undertaking of road condition assessments, for input to computerized road asset management systems (RAMS), has been systematically carried out by road authorities in South Africa since the mid 1980’s. Considerable advancements in data processing and analysis has been made in this time, but concurrent development in the actual acquisition of road condition data has not happened, with this fundamental aspect being predominantly based on manual collection methods i.e. the use of people.
Semi-automated data collection, such as the use of purpose-built surveillance vehicles equipped with cameras and profiling equipment, have been implemented by the larger road authorities over the past ten years, and whilst this is a significant improvement over manual methods, the data collected is generally limited to profile related aspects, e.g. riding quality and rut depth whilst the assessment of distress mechanisms is still undertaken with a physical visual inspection.
The development of new laser technologies in recent years has changed pavement data collection, enabling a fully automated approach to be investigated. The ability to undertake fully automated road condition data collection will offer significant and meaningful benefit to road authorities in terms of cost and time savings, safety and accuracy of data whilst road users should benefit from more timeous and appropriate maintenance interventions.
This paper presents the findings of an investigatory study towards establishing autonomous fully inclusive TMH9 compliant road condition data collection and evaluation.
