Automated Method for Airfield Pavement Condition Index Determination

Abstract

Infrastructure inspection and maintenance is a necessary, and often costly, process required for civil engineering structures throughout a project's life-cycle to ensure continued safety and serviceability. While many of these procedures have seen the introduction of technologies to assist, augment, or automate traditional methods of inspection, current practices for assessing airfield pavement serviceability remain predominately manual. Though roadway inspection has benefited from automation with the introduction of various types of sensor arrays attached to automobiles, the characteristics of airfields and their pavements have prompted research into the use of drones as a flexible, and low cost solution for automating aspects of the inspection process. As one of the largest owners and operators of airfield pavement across the globe, the United States Air Force has a unique interest in implementing such a process in a way that is both compliant and compatible with current institutional guidelines. Funded by the US Air Force Civil Engineering Center, this research proposes a novel method for conducting an automated airfield pavement condition index (PCI) survey on Air Force owned airfields using drone mounted imaging technology. Intermediate results from different stages of field testing over an auxiliary airfield located at the Air Force Academy in Colorado Springs, CO are presented and discussed in detail. Ultimately, the automated data collection and analysis developed by this study produced a PCI value of 56.5, which strongly agrees with manual inspection results that calculated a PCI value of 54 for the same runway. Also presented is a fiscal analysis of the autonomous method being proposed. Using uncertainty analysis and Monte Carlo simulation, cost estimates are given for replacing manual PCI inspections with an autonomous solution across a large number of airfield pavement assets. These estimates provide economic insights into factors that affect technological development and implementation and suggest that replacing manual methods with an autonomous system could reduce inspection costs roughly 25%.