# Complex Method of Airfield Pavement Condition Evaluation Based on APCI Index
## 1 Introduction  
Proper airfield infrastructure management can bring many benefits, both material and social. It also affects the safety of air operations in a maneuvering field. Information on its current technical condition as well as knowledge about the pavement degradation rate plays an indisputable role in the management of airport pavements. This allows you to estimate the pavement condition in the future, which in turn allows rational dispatching resources. There are known different attempts to develop a method for determining the Pavement Condition Index (PCI), enabling reliable extrapolation of the technical condition of the pavement. The original PCI method is described in the next section. A good example is paper [1], in which Iranian researchers presented the use of optimization techniques based on genetic algorithms and artificial neural networks. The designated PCI can be used later to estimate the Remaining Service Life as a parameter determining the current and future condition of the airfield pavement [2]. Such methodologies were used to assess the condition of public roads in Indonesia [3]. ANOVA methodology was used to estimate the Remaining Service Life based on the PCI. The Americans from Indianapolis used the PCI to determine the Minimum Service Level parameter for different categories of airport functional elements. This type of tool effectively supports airfield infrastructure management in determining the moments of taking repair actions on a given element [4]. This has a big impact not only on safety, but also on the costs incurred. As [PERSON] et al. showed [5], a properly selected scale of the assessment of PCI allows us to maintain the condition of the pavement, while reducing the costs incurred. The method for determining the PCI is unambiguous, and various methods can be found in both literature and practices. However, all methods have something in common. Each of them is based on observing pavement surface damages. In a majority of cases, methods used derive from the standard procedure for determining the PCI published in ASTM D5340 [6] and ASTM D6433 [7] U.S. standards and described by [PERSON] [8]. This is an up-to-date procedure developed and used by U.S. Army Corps of Engineers. At the time of writing this article, this is the most frequently used research method in both engineering and among government institutions and organizations, dealing with the maintenance of airfield pavements. The PCI is a dimensionless number from 0 to 100 describing the technical condition of the surface, where 100 means a surface in an ideal state, and 0 a pavement in a fully degraded state, unidentified to any operation. The PCI is determined based on the visual inspection of the pavement surface damage. During the review, the type of damage, their number, harmfulness and location on the selected sample of the pavement are taken into account. One of the most common procedures that comes from the PCI method is the Paver method. It was used, among others, to assess the technical condition of 20 intersections in the Samsum region in Turkey [9]. Paver is an electronic system developed by the U.S. Army Corps of Engineers. It allows you to collect and develop data obtained as a result of reviewing the visual surface and present the results on the field diagrams in a graphic form. Virginia Department of Transportation has developed Distress Maintenance Rating. It allows for the management of pavement on the \"Worst First Basis\" principle. The principle consists in creating schedules to rebuild the pavements. The presented approach to the pavement assessment is based on the PCI and Paver method, while the difference consists in determining the Virginia Department of Transportation of its own indicators describing the condition of the surface, having a mapping in the conditions prevailing in the state of Virginia. They also introduced the distress-condition index for pavement condition calculation. Moreover, they designated their own correction curves for deductions.