Abstract:
Pavement distress information is needed to assess maintenance requirements. Many
traditional systems adopted time consuming manual operations to evaluate pavement surface
distresses. To improve this method, a small network of about 73 km was prepared for a
selected region of Mumbai city, India using global positioning system. From this selected
study area twenty-eight sections are chosen for data collection. Terrestrial laser scanner
(TLS) has been chosen over visual survey method to accurately measure surface area of
distresses such as: pothole, alligator cracking, patching and ravelling. TLS is a high
definition-surveying instrument that works on the principle of Laser Scanning. Scans were
conducted on the selected sections using a Leica ScanStation CIO to capture the images of
the above mentioned surface distresses, point density, number and layout of targets, and
survey method for establishing control points. The Scan Station CIO quickly digitizes a scene
in 3D forever using both panoramic photography and 3D laser scanning, where millions of
data point digitising accurately in a few minutes. Typical target arrangements were not found
to greatly affect the resulting scan data for the equipment used in the study. The scan images
of individual distress are processed in Cyclone software, readily available within the
instrument, and measured surface area of these distresses after cleaning registered point
cloud. Straight edge has been used to measure rutting. In this paper surface area of these
distresses were analysed using subjective rating method called road condition index (RCI). A
road condition index is the weighted average of all urgency indexes, product of degree and
extent of distress. RCI represents in terms of number that indicates the overall performance of
the study area which consists of number of distresses such as: patching, rutting, ravelling,
potholes and cracks. The value of RCI varies between 1 for a new pavement with no distress
to 25 for a failed pavement. Rating approach method in RCI is used to facilitate the
prioritization for all 28 sections. Finally ranking for each section is determined based on the
obtained priority rating values of each section.
