Detection and classification of distresses is a fundamental activity in the road pavement management. Even in the early stages of deterioration, road pavement needs to be monitored to identify problems, evaluating the actual conditions and predicting what the future conditions will be. Monitoring activities through manual/visual inspections are time consuming, costly and cause of safety concerns. For these reasons, distress identification is usually limited to few sections randomly selected. The introduction of new high efficiency equipment for distress detection and classification is opening new perspective in road pavement analysis and management. Automatic pavement monitoring and Mechanistic design are introducing new pavement performance indicators and criteria for distress classification. Previous studies show lack of correlations between indexes derived from manual and automatic pavement monitoring. Therefore, capability to derive manual distress parameters from automatic monitoring systems is of great interest in the definition and testing of criteria and methodological approaches. In this paper, a background is reported by referencing examples of North American and Italian tests for the detection and classification of distresses from manual survey and capabilities of the state-of-the-art Automatic Road Analyzer (ARAN 9000) as well. An infield experiment and calibration of a Probabilistic Neural Network Classifier is presented for deriving distress measures from automatic systems.
|Titolo:||From manual to automatic pavement distress detection and classification|
|Data di pubblicazione:||2017|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|