# Complex Method of Airfield Pavement Condition Evaluation Based on APCI Index
## 1 Introduction
The experts make visual assessment, and thus a human factor appears, which can significantly affect the final result of the analysis. In order to ensure quality at a suitable level, guides have been created, which present in detail how to carry out the inspection and how to proceed with the data collected. Such a document was developed, among others, for the needs of Federal Highway Administration [21] and Indiana Department of Transportation [4]. Italians [22] suggested their own extension of the damage directory presented in ASTM D6433 [7], adjusting it to the needs and morphology of road surfaces in the Italian cities. Two types of damage (cavities and trees roots have been added) and a new deduction curve has been added as a function of damage density. In addition, the entire data processing cycle has been largely automated by implementing the application in the Visual Basic environment. To eliminate the human factor from the data collection process, it strives to completely automate. In article [23], the author, as an example, shows ARAN, which enables automatic road analysis. The system is based on the data being obtained during the measurement of the pavement damages. Data are harvested in the form of surface images made of cameras and a three-dimensional surface image obtained as a result of three-dimensional scan. Unfortunately, the obtained data are later processed manually by humans, which still affects the quality of information on the actual technical condition of the rated surface. A similar approach was presented by [24]. Researchers used laser sensors for roadway pavement scanning. They also included model deflections and the evenness of pavement. Unfortunately, they did not obtain enough accuracy for airfield pavements. A huge step for autonomous calculation of pavement condition survey was done by [PERSON] et al. [25]. They used convolutional neural networks for pavement condition evaluation and achieved prediction accuracy of 98%. Nevertheless, it still refers only to pavement surface condition--its visual damages. The methodology for assessing the condition of airport pavements was developed in the Military University of Technology [26; 27], which, to determine the technical condition of the surface, takes into account such indicators and parameters as:  
* Damage indicator of the surface,
* Pavement repair indicator,
* Technical consumption indicator,
* Surface friction,
* Evenness of surfaces. By means of the above-mentioned parameters, a pavement condition can be predicted that helps in planning maintenance undertakings. This approach specifies a set of classification parameters of the pavement for listed indicators, as well as friction and evenness criteria, but does not take into account the decisive indicator, which is the load-bearing capacity parameter. Another procedure was created at the Air Force Institute of Technology, which assumes that the measure of the use of the airport surface is the usefulness of the pavement Present Serviceability Index [27; 28]. To determine the value of this index, it is necessary to perform research and measurements of local technical parameters of the airport surface, such as:  
* Survey of the surface damages of pavements,
* Survey of fixed surfaces,
* Measuring the longitudinal evenness of the surface,
* Measuring the transverse evenness of the surface. It can be noted that the assessment of the useful condition of the pavement is not taken into account like the load capacity and the anti-skid properties of the assessed airfield pavements. In the literature, we can find more positions that address Pavement Condition Index and similar methods. However, all of the present methods use more than two or three pavement technical condition parameters. The huge number of the existing works concerns mainly visual inspection of surfaces. To sum up, it can be stated that most of the currently known and currently used methods for assessing technical condition are intended for road pavements.