During an evaluation of airfield pavements at Austin-Bergstrom International Airport (ABIA) in Austin, Texas, concerns regarding the performance of one particular runway—17L/35R—were brought to the attention of the pavement engineering firm The Transtec Group. The runway was experiencing distress in the touchdown areas earlier than ABIA anticipated. It was determined that a more detailed evaluation on the runway pavement was necessary to determine the potential impacts, limits, and appropriate course of action.
Then, RS&H, the national architecture, engineering and consulting firm in charge of the project, hired Transtec to perform an evaluation of the airfield pavements. RS&H was interested in using Rolling Dynamic Deflectometer (RDD) testing to enhance the traditional pavement evaluation and knew Transtec has extensive experience with evaluating and interpreting data from the RDD. Transtec tested and analyzed the RDD data collected, and determined that the runway was in overall good condition and complete rehabilitation was not required; but the findings verified that there were isolated areas experiencing accelerated distress that would need to be reconstructed. The continuous nature of the deflection data allowed Transtec to pinpoint the areas that were performing the worst and find the exact limits of the zones needing repair.
To support the airport’s pavement management program (PMP), the primary evaluation included data collection of:
• Runways
• Taxiways
• Maintenance ramps
• Portion of the general aviation apron
Data gathered was used to determine:
• Structural capacity
• Evaluate support conditions
• Aid in forensic analyses
• Select design alternatives
• Select rehabilitation alternatives
Deflection Testing
Non-destructive test (NDT) equipment can be used to evaluate pavements by measuring the pavement response (deflection) to specified loading conditions to help owners understand how their pavement is performing given the current or future loading so owners can better strategize their expenditures to balance costs and pavement performance. For this project, two methods were selected to perform deflection testing and assess pavement structural integrity:
1. Heavy Falling Weight Deflectometer (HWD)
—measures pavement deflections at discrete locations, every 50 to 250 feet depending on the project requirements and available budget, and entails:
• Dropping a weight on to a loading plate
• Applying a transient force on the pavement
• Spacing sensors at set intervals
• Measuring the pavement response, or deflection
• Enabling users to identify differences in pavement response
However, it would be necessary to increase the testing frequency since discrete testing makes it impossible to define where changes occur and it may miss other localized issues between test locations. At many airports, the duration of testing is critical factor, so any technology that can reduce disruption to operations can be a cost saving benefit.
2. RDD testing
—done to support the HWD testing—identifies areas where additional discrete testing using an HWD is needed. Developed at the University of Texas Austin, the RDD is a mobile, truck-mounted device, that performs continuous deflection testing of pavement systems:
• Generates and measures large dynamic forces
• Collects deflection measurements continuously
• Applies to the pavement through loading rollers, as the truck continuously moves along the pavement
• Determines deflections—indicators of the mechanical properties of the pavement system—induced by the dynamic loads are measured with multiple, specially designed rolling sensors
• Constantly moving
• Allows for a higher resolution to the data (more data points in less time)
Using the RDD allows users to:
• Easily pinpoint exact locations where support conditions change
• Assess the overall structural response and service life of a pavement structure
• Differentiate the relative stiffness of different regions
• Delineate the regions of the pavement influenced by joints, cracks, and weak regions
• Assess the performance of cracked or jointed regions
• Look for trends in pavement deterioration across a continuous data set
• Identify differences in deflection response related to various construction features (fills vs. cuts; drainage structures, differences in pavement structural section, etc.)
• Conduct future structural evaluations
• Interpret results
• Optimize future pavement maintenance and rehabilitation
• Use results to assess the remaining life and support conditions for specified loading and determine pavement classification numbers (PCN)
Testing at ABIA
One of the key reasons Transtec selected the RDD was to reduce the number of test locations required by the HWD, while increasing the amount of data obtained and providing a much clearer and more detailed picture of the actual structural condition of the pavement. This was especially important in the evaluation of Runway 17L/35R as the goal was to determine the limits of possible rehabilitation. The RDD provided continuous and detailed deflection data for runway pavements tested and the data was then used to help select specific HWD test locations for a more detailed analysis:
• Large volume of RDD data was collected for each runway, taxiway, the maintenance ramp, and section of the general aviation apron
• Data evaluated at a deeper level on Runway 17L/35R due to the concerns of localized pavement distress
• Differences were identified in deflection response related to various construction futures—fills, cuts, drainage structures, and differences in pavement structural selections
• Making it useful for conducting future structural evaluations, interpretation of results, and planning for pavement maintenance and rehabilitation
Finally, deflection profiles collected were processed and converted to shapefiles for viewing in ArcGIS Pro. These profiles, high-resolution aerial imagery, joint layout diagrams, and other useful information, provided valuable insights into the pavement condition and were used to aid in the selection of HWD testing areas
The figure above shows an example of the continuous data along Runway 17L/35R. The individual peaks represent the higher deflections along the pavement joints.
The figure above provides an example of how the data was evaluated in GIS and includes an overlay of pavement condition data sample units (color coded red to green), mid panel deflections from the RDD (bars), joint locations, and previous panel repairs (hatched boxes)—Shows where the deflections were higher than the baseline average. The continuous RDD data shows that additional panels should be replaced (not originally replaced) and that some of the repaired areas may not be performing adequately.
To provide an initial verification of the structural support of the pavement, additional life analysis was conducted using the Federal Aviation Administration (FAA) software “FAA Rigid and Flexible Iterative Elastic Layered Design (FAARFIELD)“—an airport pavement design and evaluation software developed by the FAA. The main inputs in the analysis included the airplane fleet mix, modulus values for the various pavement layers, and the as-built thickness. The results of the analysis confirmed that the majority of the pavement was structurally sound, but there were isolated areas in which the pavement needed some form of rehabilitation or maintenance.
Conclusion
The primary focus of the evaluation was to provide baseline data to be incorporated into the airport’s PMP and to help with planning and budgeting of future maintenance and rehabilitation activities. The continuous deflection data from the RDD allows for a quick comparison of the performance of the various surfaces measured.
For the conclusion, additional testing, and much more information, please go to the Transtec article titled “The Transtec Group’s Continuous Testing Data Saves Airport from Costly, Major Reconstruction”: Case study: https://www.thetranstecgroup.com/rdd-continuous-testing-data-saves-airport-costly-major-reconstruction/?utm_medium=email&utm_campaign=July%20Newsletter&utm_content=July%20Newsletter+Version+A+CID_276175c77bc30a1718475208bf0824a0&utm_source=Email%20Campaign%20Monitor&utm_term=Read%20the%20Case%20Study