Terrestrial LiDAR derived 3D point cloud model, digital elevation model (DEM) and hillshade map for identification and evaluation of pavement distresses

The periodic assessment of pavement condition is one of the important requirements in ensuring not only the service life of the pavement but also for the safety of motorists as pavement distresses like potholes led to many fatal accidents in recent years in India. The traditional approach of manual evaluation is a cumbersome and time-consuming process which can be replaced by latest technologies like LiDAR. Literature on use of Terrestrial LiDAR for distress detection are very limited and even the reported studies have used mostly the raw point cloud data from LiDAR for detecting only selected distresses. However, there are many outputs which can be derived from Terrestrial LiDAR data like Digital Elevation Model (DEM), Hillshade, etc. which are not explored much. Hence in the present study, an attempt has been made to utilize all the possible outputs of Terrestrial LiDAR data, i.e., raw 3D model from point cloud data and LiDAR derived DEM and Hillshade to detect 15 pavement distresses in both bituminous and concrete pavement for a case study area of Vellore in Tamilnadu, India. Their suitability also was compared in order to find which output would be more suitable in identifying a particular distress on the road. The results revealed that Hillshade maps can detect all the pavement distresses except cracks when compared to 3D model and DEM. For the detection of pavement cracks, an image processing based solution using LiDAR data was tried out and was found to perform well in detecting the alligator cracks. The distress parameters such as length, width, depth, etc. were also calculated using LiDAR data and compared with the field observations. The error was found to be only few cms and thus indicate that the Terrestrial LiDAR can be employed to detect the pavement distresses accurately than the conventional manual methods.