Date of Award
Master of Science in Engineering Management
Department of Systems Engineering and Management
Steven J. Schuldt, PhD
Airfield pavements are a critical component of the global transportation network that provide a platform for national defense. Preventative and corrective maintenance activities are founded upon accurate expectations of degradation. The leading pavement management software creates degradation predictions from pavement groups using age as the IV and current state conditions as the DV. For this work, a framework is created and implemented that utilizes a PCR model to build upon accepted practices for degradation modeling to enhance and possibly augment future prediction capabilities. The model was applied to pairs of location and pavement family and reveals several findings: the selected climatic variables describe 74-93% of pavement degradation across 1,995 pavement sections constructed between 1985-2019; the effects from climatic factors are nonstationary; and environmental factors are more impactful than aircraft passes, with between 2-15% improvement of model skill in the pavement family that supports the most aircraft operations when comparing two datasets of 266 pavement sections between 2010-2019. The created framework discovered that freeze-thaw, solar irradiance, precipitation, and sustained wind were commonly significant factors in describing degradation variability and can be applied to any large airport with data availability to determine local sources of degradation and improve pavement design sustainability.
DTIC Accession Number
Fortney, Evan M., "Improving Airfield Pavement Degradation Prediction Skill with Local Climate and Traffic" (2021). Theses and Dissertations. 5054.