Creating Condition Aware Pavement Condition Predictions

Healthy airfield pavements are a critical component of the global transportation network, and a vital platform to project power worldwide. With more than 2-billion-ft² of airfield pavement in its inventory and the centrality of sortie generation critical to its core mission, the U.S. Air Force invests heavily in recurring maintenance and periodic rehabilitation projects. However, proactive planning of repair activities is difficult because pavement degradation rates are uncertain and dynamic.

Asset managers use degradation models in the absence of a recent physical evaluation to plan maintenance and repair. PAVER is the primary tool used for all pavement lifecycle management; however, a limitation is that it neglects potential degradative effects of exposure to aircraft loading and climate effects, such as freeze-thaw, solar irradiance, wind, and precipitation.

To determine the degree to which these factors explain variability in pavement condition, a collaborative effort between the Air Force Institute of Technology and the U.S. Army Engineer Research & Development Center’s Construction Engineering Research Laboratory used local, historical climate, pavement condition, and aircraft pass data to inform statistical model sets that hold the potential to add robustness to current prediction models.

The results suggest that up to 93 percent of pavement degradation is explained by climate. This could enable reduced frequency in costly and disruptive physical assessments, provide pavement managers with increased prediction accuracy, and give mix designers climate design objectives.

COMPARING CLIMATE DATA

To begin the Air Force study, the researchers used data from a spread of selected installations to inform statistical model sets that hold the potential to increase the accuracy of existing prediction models. The team created a statistical, regression-based framework that can be applied to any large airport. This framework and statistical process was performed on three datasets that span major climate zones and house varying airframes.

Testing multiple datasets with different timeframes and variables shows a wide range of applicability for the created framework, and it provides insight into how time and conditions interact.

Impacts of Weather. The first framework application establishes whether climatic influences are significant and which specific types of weather effects contribute to poor pavement conditions. The work evaluated a dataset with five climate variables at 14 installations from 1985 to 2019. The second model set sought to determine if climate influences remain the same across time by comparing the first 35-year dataset to a 10-year dataset at nine installations from 2010 to 2019. The final set of models included both climate and aircraft pass data from 2010 to 2019 to investigate the influence of aircraft on pavement degradation.

The climate variables used were freeze-thaw days; water equivalent precipitation in inches; snowfall depth in inches; days with sustained wind speeds above 10-mph; and solar irradiance. These variables have different values with equally varying units. They were normalized in order to be compared regardless of units or magnitude, then summed up by year. Lastly, each annual value was compiled throughout time so cumulative effects could be discovered at each location.

The first data set comprised 1,995 pavement sections over 35 years, and the results were significant across most pavement families and locations. When applied to each pavement family at each installation, the regression model revealed which climate variables significantly impacted each location. Solar irradiance was commonly significant in the southern states. High-level sustained wind was significant in many locations. Freeze-thaw had a large effect in 10 of the 14 locations studied. The results from the first dataset highlight that pavement design should be focused at the installation level by localized climate conditions to best meet their needs and fight the local sources of deterioration.

This result is somewhat surprising, especially considering that traditional wisdom suggests condition is primarily a function of use. It could also indicate that pavement design efforts are successfully accounting for expected aircraft loads.

Additional Variables. The purpose of model sets 2 and 3 was twofold: to test whether the importance of climate variables and model skill were sensitive to time; and to determine whether traffic data meaningfully impacted model skill. Many of the influences were similar when applied to different time spans. Minot, N.D., notably, had the same effects between the two model sets. The significant climatic variables did change in every other location, displaying how weather trends change over time.

Moreover, climate plays a much larger role in pavement deterioration than passes. When only considering the most important pavement family made from rigid materials (the first 1,000-ft of runways and primary taxiways), the model could account for 36 to 80 percent of the pavement degradation without including aircraft passes. By including passes as an independent variable alongside climate inputs, the resulting skill improvement for this family is expected to increase. However, the improvement was minimal, with one location decreasing in skill by 2 percent.

This result is somewhat surprising, especially considering that traditional wisdom suggests condition is primarily a function of use. It could also indicate that pavement design efforts are successfully accounting for expected aircraft loads. Regardless, this revelation justifies renewed efforts to make pavement design more robust by considering local climatic elements when creating mix designs. Exposure to the elements is the most impactful source of deterioration.

HANDLING AN UNCERTAIN FUTURE

A major challenge for asset managers is how to handle changing future conditions. Nonstationary projections of climate require modeling software to be adaptive if they are to be trustworthy. Climate change projections or future mission requirements introduce risk to which current systems cannot adapt. Moreover, the system framework created through this collaboration directly challenges the concept of static climatic conditions—and it can be adapted for projected conditions, helping asset managers mitigate the effects of an uncertain future.

The discoveries made through this research collaboration align with Department of Defense goals to use data-driven, deliberate, and systematic approaches to lifecycle management of pavements. The research team easily found location-matched data types for historical climate and pavement conditions partly thanks to the Air Force Civil Engineer Center’s successes with centralized data collection efforts over the last decade. The main limitation to the second and third framework applications, with less than 300 pavement sections between nine locations, was the fact that the Air Force had only kept centralized aircraft pass data since 2010. More and different data should be collected in order to keep making sustainable pavement investment decisions.

Looking ahead, the process applied through the study could be replicated for other environmentally exposed assets. Accounting for exposure at their specific locations and adapting to changing future conditions could improve the predictive strength of not just PAVER, but BUILDER as well.

Capt. Evan Fortney, M.SAME, USAF, is PCE Instructor, Lt. Col. Justin Delorit, Ph.D., P.E., PMP, M.SAME, USAF, is Assistant Professor, and Lt. Col. Steven Schuldt, Ph.D., P.E., M.SAME, USAF, is Assistant Professor, Department of Systems Engineering & Management, Air Force Institute of Technology. They can be reached at c16evan.fortney@gmail.com; justin.delorit@afit.edu; and steven.schuldt@afit.edu.

[This article first published in the September-October 2021 issue of The Military Engineer.]