From manual to automatic pavement distress detection and classification
| Autor: | Alfonso Montella, Salvatore Cafiso, Carmelo D'Agostino, E. Delfino |
|---|---|
| Přispěvatelé: | Cafiso Salvatore, D’Agostino Carmelo, Delfino Emanuele, Montella Alfonso, Cafiso, S., D'Agostino, C., Delfino, E., Montella, A. |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Operations research Computer Networks and Communications 0211 other engineering and technologies Neural Network Transportation 02 engineering and technology Pavement Condition Index Machine learning computer.software_genre Automatic Road Analyzer Probabilistic neural network Artificial Intelligence 021105 building & construction 0502 economics and business 050210 logistics & transportation Artificial neural network business.industry 05 social sciences Pavement Management System Pavement management Monitoring system Distress classification and rating Modeling and Simulation Distress Computer Networks and Communication Performance indicator Artificial intelligence business computer |
| Zdroj: | MT-ITS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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