Abstract

AbstractStreet defects, such as potholes and sunken manholes, in general develop quickly compared to other pavement distresses, such as cracking and rutting. Those street defects can result in vehicle damage. This paper proposes an automated and innovative method to obtain up-to-date information about those street defects with the use of a mobile data collection kit mounted on vehicles. In each mobile data collection kit, a triaxial accelerometer and global positioning system sensor collect data for the detection of street defects. A local algorithm is embedded in the mobile data collection kit to increase the efficiency of a local data logging process and to perform a preliminary detection of street defects. At a back-end server, a more precise street defect detection algorithm enhances the performance of the proposed monitoring system by integrating data collected from multiple sensor-equipped vehicles. The street defect detection algorithm at the back-end server relies on a supervised machine learning ...

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