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Pavement Distress Detection Methods: A Review

Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, 00184 Rome, Italy
Department of Engineering, Roma Tre University, 00154 Rome, Italy
Author to whom correspondence should be addressed.
Infrastructures 2018, 3(4), 58;
Received: 28 September 2018 / Revised: 22 November 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
(This article belongs to the Special Issue Sustainable Transportation Infrastructures)
The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure. View Full-Text
Keywords: pavement distress; pavement management; distress identification; data collection system pavement distress; pavement management; distress identification; data collection system
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MDPI and ACS Style

Ragnoli, A.; De Blasiis, M.R.; Di Benedetto, A. Pavement Distress Detection Methods: A Review. Infrastructures 2018, 3, 58.

AMA Style

Ragnoli A, De Blasiis MR, Di Benedetto A. Pavement Distress Detection Methods: A Review. Infrastructures. 2018; 3(4):58.

Chicago/Turabian Style

Ragnoli, Antonella, Maria R. De Blasiis, and Alessandro Di Benedetto. 2018. "Pavement Distress Detection Methods: A Review" Infrastructures 3, no. 4: 58.

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