01662662

Component

Light Detection and Ranging (LiDAR) systems have been increasingly used in transportation project planning and development, construction, and asset management as a means for collecting field measurements rapidly and safely. However, processing of large amounts of data collected by mobile LiDAR systems (MLSs) remains tedious and time-consuming, especially at the network level. For MLSs to be used efficiently in roadway inventory and condition assessment, automated methods are needed. This paper describes the development and validation of a computational method for automated identification of pavement surface type from data collected through MLSs. The developed method uses the Received Signal Strength Indicator (RSSI) to identify surface type because pavement surface characteristics such as texture, granulation size, coloration, and porosity affect laser reflectivity, leading to different RSSI values. The studied surfaces include concrete, dense graded asphalt, open graded asphalt, and seal coated surface. Generally, the accuracy of the developed method was at least 83%, depending on the surface type. It is hoped that this method will improve the efficiency of mobile LiDAR systems for roadway inventory and condition assessment.

This paper was sponsored by TRB committee AFD10 Standing Committee on Pavement Management Systems.

18-03111

English

4p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Accuracy; Condition surveys; Laser radar; Surface course (Pavements)

Automated identification systems; Computational methods; Field measurements; Received signal strength indicator

Highways; Pavements

Transportation Research Board Annual Meeting 2018 Paper #18-03111

TRIS, TRB, ATRI

Jan 8 2018 10:45AM