University of North Carolina at Charlotte. Transportation Policy Studies

United States. Dept. of Transportation. Research and Innovative Technology Administration

USDOT/RITA DT0S59-07-H-005

Tech Report

United States

University of North Carolina at Charlotte. Transportation Policy Studies

This report describes Phase Two enhancement of terrestrial LiDAR scanning for bridge damage

evaluation that was initially developed in Phase One. Considering the spatial and reflectivity

information contained in LiDAR scans, two detection algorithms were developed to document

the extent of bridge surface damage. The first algorithm introduced in this report is based on

spatial information. This algorithm can detect various damages on a bridge structure according to

the extent of surface damage. Because some damage may involve deep area losses that are not

along the laser line-of-sight (LOS), an experiment is conducted to find out the best location to set

up a LiDAR. The two damage detection algorithms are presented and compared with scans on

actual bridge damages to evaluate their effectiveness.

A further study estimated the potential of including reflectivity data to improve defect detection.

The addition of reflectivity in damage diagnostics was determined to be useful for defect

detection of curved surfaces. The study shows that the reflectivity of LiDAR scan could be used

to support the automatic defect detection in bridge inspection by combining it with the current

position-based algorithms.

A total of 88 bridges were studied during the two-year project period. Bridge scan data were

analyzed using a spatial-based damage detection algorithm, which shows that the LiDAR

application is a useful supplement to traditional visual inspection. Possible improvements in the

future research could be achieved by optimizing existing algorithms.

PDF

DT0S59-09-H-0007

US Transportation Collection