Abstract
Road surface distress is an unavoidable situation due to age, vehicles overloading, temperature changes, etc. In the beginning, pavement maintenance actions took only place after having too much pavement damage, which leads to costly corrective actions. Therefore, scheduled road surface inspections can extend service life while guaranteeing users security and comfort. Traditional manual and visual inspections don’t meet the nowadays criteria, in addition to a relatively high time volume consumption. Smart City pavement management preventive approach requires accurate and scalable data to deduce significant indicators and plan efficient maintenance programs. However, the quality of data depends on sensors used and conditions during scanning. Many studies focused on different sensors, Machine Learning algorithms and Deep Neural Networks tried to find a sustainable solution. Besides all these studies, pavement distress measurement stills a challenge in Smarts Cities because distress detection is not enough to decide on maintenance actions required. Damages localization, dimensions and future development should be highly detected on real-time. This paper summarizes the state-of-the-art methods and technologies used in recent years in pavement distress detection, classification and measurement. The aim is to evaluate current methods and highlight their limitations, to lay out the blueprint for future researches. PMS (Pavement Management System) in Smarts Cities requires an automated pavement distress monitoring and maintenance with high accuracy for large road networks.
Highlights
Damages detection and measurement in bridges, buildings and roads are not a recent focus of researches
In order to make a significant comparison between algorithms, some indicators were defined and measured
Four situations can be defined: S1: Picture/Pixel damaged according to the dataset and the algorithm; is counted as the percentage of reality (TP) (True Positive)
Summary
Damages detection and measurement in bridges, buildings and roads are not a recent focus of researches. Multiple studies [22] were conducted during the last decades to determine adaptable equipment and solutions. [23] keeping pavement surfaces in a good condition via a low-cost solution is still a big challenge. There is no doubt that concepts of the future [24] (Smarts Cities, Smarts Roads, Smart Traffic, Intelligent Transportation Systems, etc.) are looking for automation, precision, energyefficient and security while providing humans with the best services. Besides the construction of multiple road networks, several cities are suffering congestion and overloading. For example [25], the number of private cars in China was 320 million in 2018; fifty-eight cities hold more than one million cars and seven cities support more than three millions
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