A series of experiments by University of Arkansas researcher Kelvin Wang showed that digital acquisition of highway data overcomes the comparability problems of different analysis methods and gives highway engineers better information for allocating scarce resources.
Cracks in roads cause risks for motorists and substantial highway repair costs. Highway engineers use different methods to evaluate cracks and determine when they should be repaired, but the methods produce different levels of detail and their results cannot be easily compared
Wang, associate professor of civil engineering, used his digital highway data vehicle (DHDV) to collect data from a 2.8-mile section of road. The data was then used to evaluate three primary distress analysis systems: the new American Association of State Highway and Transportation Officials (AASHTO) interim protocol, the World Bank’s Universal Cracking Indicator (CI) and the Texas DOT method. Four sets of data were acquired and analysed.
The DHDV uses a GPS device to constantly log its location. Its onboard parallel computers can store and analyse thousands of miles of data at high resolution. The acquired data can be transferred to a networked data warehouse and accessed from any network location.
‘The result of the analysis demonstrates the advantages in consistency of using the automated system for distress survey,’ said Wang. ‘The automated system is able to produce distress data while digital pavement images are being acquired at speeds over 60, achieving real-time processing with computing facilities on board the DHDV.’
The cracks revealed in the digital images were categorised by location, geometry and orientation and stored in a database. The three distress protocols were incorporated into the system, so the indices were immediately available for analysis. For this study, researchers only acquired data within the driving lanes.
‘Even though there are technical differences among the three methods, we expected that the results from the automated system should be comparable and should reflect the condition of the pavement,’ said Wang.
Results from the three methods showed the same distribution pattern for the extent of cracking on the same roadway segment. All three methods indicated that the same subsection of the roadway had the most crack damage.
However, each method provided different details about the roadway cracks. The CI produced a single number to indicate the severity of cracking in a roadway segment, while the Texas method gave details on the number of cracks, crack lengths and percentage of alligator and block cracks. Only the AASHTO method gave an indication of the severity of the cracks for both wheel-path and non-wheel-path portions of the lane.
‘The CI can be used for general pavement condition evaluation where the cracking details are not important,’ Wang explained. ‘The other two methods can be used if more detail is required. One problem is that each method uses a different algorithm, which makes direct comparison of their output difficult. But the difficulty is even more obvious for manual surveys.’
Almost from the moment they are built, highways must be examined regularly for signs of wear – a process that currently produces mountains of data that are difficult to access and interpret. The most widely used method to survey surface distress of highway pavements – human observation – is labour-intensive, error-prone, and hazardous.
In addition to eliminating problems associated with direct human observation, the DHDV can overcome the comparability problem among the distress analysis methods, according to Wang.
‘The consistency of the DHDV data allows highway engineers to use one or all of the analysis methods, depending on the level of detail that they need,’ Wang explained. ‘With these tools, engineers can easily identify distresses in the roadway, determine the exact scope of the problem and make cost-effective decisions.’