Pavement Condition Index Analysis: Total versus Sample Calculations Using Vehicle-Equipped Imaging

Abstract: The continuous growth of air transport has intensified the demand for airport infrastructure due to the increasing volumes of traffic on runways, creating the need to adopt effective maintenance strategies that ensure operational quality and safety. To achieve these goals, it is necessary to utilize data collection methods that can rapidly and accurately assess pavement conditions. Technological innovations which surpass traditional approaches in terms of speed and precision have gained prominence in this context. One widely accepted method for evaluating runway conditions is the Pavement Condition Index (PCI).
In this study, the main runway of Amílcar Cabral international airport, located at Sal Island in Cape Verde, was evaluated using georeferenced images collected by a vehicle equipped with a Garmin Elite camera, two 20 mW lasers, and geolocation devices. The data were processed in a Geographic Information System (GIS) to identify surface distresses in the pavement. The runway was divided into three sections (A, B, and C), totaling 273 sampled units inspected through the indirect data collection method. Additionally, 43 sample units were randomly selected according to the airport-specific PCI method and assessed using the traditional visual data collection method conducted on-foot. These same 43 units were also evaluated by two inspectors in the GIS environment, utilizing the georeferenced images obtained from the vehicle prototype.
The PCI was calculated for both the 43 sample units and for the entire runway. Analysis of the PCI values (total and sample) was carried out, verifying compliance with regulatory standards. This study also investigated the potential biases among inspectors in identifying distresses by comparing evaluations made using the traditional and the vehicle methods, conducted by two different inspectors, to analyze variability in results and highlight the importance of rigorous training to ensure greater accuracy.
The results have indicated a significant improvement in the precision and reliability of evaluations when utilizing the vehicle data collection method combined with image analysis in a GIS environment. Thus, this approach stands out as a promising alternative to on-foot inspections, allowing for data reassessment and facilitating quality control of measurements. In addition, it reinforces the importance of adequate training for inspectors, especially in traditional methods where distresses and their severity levels cannot be reviewed. The vehicle method makes the process more agile and efficient, contributing to more consistent evaluations and more effective maintenance strategies. Furthermore, the results support the adoption of this method in the pavement condition assessment component of Airport Pavement Management Systems (APMS).