Complex Method of Airfield Pavement Condition Evaluation Based on APCI Index

Abstract

Airfield infrastructure management should be effective. First of all, renovation works should be planned and carried out so that the equipment or resources, both financial and personal, are used optimally. The basis for planning funds should be knowledge about the actual pavement condition and, more importantly, the future pavement condition. The main goal of the paper is to present a complex method of technical condition assessment of airfield pavements made of cement and asphalt concrete, as well as natural pavements, which are part of the ground maneuvering field on each airfield. The authors propose a new method of assessing the technical condition of the pavement based on the Airfield Pavement Condition Index (APCI). Compared to existing methods, based mostly on the visual assessment of the pavement surface damages, the APCI method also includes the inventory of the repairs and diagnostic tests such as the assessment of the load capacity, evenness, anti-skid properties and the surface layer tensile bond strength. The presented mathematical models enable automation of pavement assessment process. That can lower costs and speed up whole evaluation.

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 Sharaf 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 Shahin [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. The results of their work are placed in reports [10,11] and paper [12].

The evaluation of the technical condition of the PCI method is based only on its visual assessment. This has an indisputable advantage, which is the speed of evaluation, but unfortunately it has disadvantages. One of the largest is to receive incomplete information on the real technical condition, which does not enable us to assess the condition in a comprehensive manner. Many scientists have improved the method with additional parameters. Such a solution was adopted by Arhin, Williams, Ribbis and Anderson [13]. Their method takes into account, in addition to the visual pavement surface assessment, also the International Roughness Index. This is a parameter describing the evenness of the surface, i.e., the properties of the surface determining the comfort of travel. In addition, the authors presented the correlation between the PCI and the International Roughness Index. At the same time, they developed a model to estimate the PCI based on the measured International Roughness Index. Similarly, scientists from Kazakhstan presented the proprietary method for the assessment of pavement condition [14]. In addition to the International Roughness Index, they also used the measurement of elastic deflections and included macroxes, ruts and cracks. They described the resulting pavement condition by the Pavement Condition Rating parameter, enabling the pavement evaluation in a functional context, which is the basis in making maintenance decisions. To manage the road network in the Indian city Noida, there is a developed road surface assessment method, which is based on the Overall Pavement Condition Index [15] and includes four indicators: Pavement Condition Distress Index, Pavement Condition Evenness Index, Pavement Condition Structural Capacity Index and Pavement Condition Skid Resistance Index. The method consists in determining indicators individually, and then based on them, Overall Pavement Condition Index is calculated.

Some of the researchers calculate PCI from other pavement condition indices. One of the examples can be work conducted by Elhadidy et al. [16]. They prove that it is possible to link International Roughness Index with Pavement Condition Index. They use the Long-Term Pavement Performance database to make a regression model that links PCI with International Roughness Index. The coefficient of determination was 0.995. Another work [17] presents the correlation between PCI, Structural Condition Index and Foreign Object Damage Index. By using the abovementioned indices, researchers try to predict the Remaining Service Life for Portland cement concrete and asphalt concrete pavements. Their study shows that the use of PCI, Structural Condition Index and Foreign Object Damage Index for planning of maintenance and rehabilitation at airfield pavements may not be fully replaced by each other. Nevertheless, such correlation exists.

An important factor, due to the optimization of the time assessment of the pavement technical condition, is the automation of the process. An example is the study [18], in which the authors present a weighing approach to the PCI model. It is common in a traditional PCI method to determine the parameter you are looking for in a visual assessment, while the method of calculating is a bit different. Each damage enters the model with a suitable weight, and the entire process can be programmed. A similar approach presented Wesołowski, Barszcz and Blacha [19] and Zieja, Barszcz, Blacha and Wesołowski [20]. In this case, in addition to the traditional record of damage to the assessment of the degradation degree, data obtained as a result of the inventory of the pavement repairs was used. Both the damage and the repair of the pavements enter the model with a characteristic weight for itself. Weights have been determined based on the harmfulness of a given damage or a given repair.

The basis for the proper estimation of the PCI is to collect reliable and up-to-date data of the pavement condition. 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 Yang 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. However, it lacks contemporary, overall solutions that could be successfully used in full range for airport surfaces. Therefore, the abovementioned ways to assess road surfaces are very often adapted to airfield applications.

Bearing in mind the above and the specificity of the work and the operation process of airfield pavements, the authors proposed a new, systemic method of comprehensive assessment of the technical condition of the airfield functional element surface, based on the APCI. The determined surface index is based not only on surface damages of the airfield pavements, but also takes into account the survey of repairs and technical parameters, such as load capacity, anti-skid properties, evenness and the surface layer bond strength. This approach gives a wider view of the actual technical condition of the airfield pavement and at the same time takes into account a significant aspect of the safety of air operations by aircraft.

In the next sections, a few pavements assessment methods based on the Pavement Condition Index are presented. In the results section, a complex method of pavement condition evaluation based on APCI is described. At the end, we discuss the presented method and reach conclusions.

2. PCI Method

The evaluation of the technical condition of the airfield functional elements pavement can be performed on the basis of several developed and implemented ways.

One of the most commonly used methods in the world is a method using the PCI. This is a standard procedure developed and used by the U.S. Army Corps of Engineers, which is based on the inventory of surface damage pavement [29,30]. During the review, the type of damage is taken into account, as well as their number and harmfulness. PCI is a unitless number from 0 to 100 describing the technical condition of the pavement, where 100 means a pavement in a perfect condition, and 0 a pavement in a fully degraded state, not allocated to any age. The scale of the pavement assessment based on the PCI is shown in Figure 1 [6]. The same figure also presents a generalized PCI scale for a coarse assessment of the pavement.

The pavement inspection takes place in a strictly defined manner, according to the procedure contained in previously quoted standards. The first stage of work is to divide the airfield pavement for research samples–functional elements of the airport, i.e., on the roadways, taxiways and aprons. Then, each of the elements are divided into a single sample. The division takes place in accordance with the instructions. All surfaces made of cement or asphalt concrete are divided into samples with an area of 5000 ± 1000 sq. ft., which corresponds to approx. 460 ± 180 m². Each of the samples is considered individually. Due to the fact that the examination of all samples is time-consuming, which determines cost of work, it is possible to reduce the number of samples. The advantage of such a solution is to reduce the costs related to inspection, but at the expense of the quality of the results obtained. The minimum number of test samples n is calculated from Equation (1) [28]:

$$n=\frac{N·{ s}^2}{\left(e^2/4\right)\left(N-1\right){+s}^2},$$

(1)

where N is a total number of samples resulting from the pavement division, e is acceptable Estimation error of the PCI, and s is a standard deflection of the indicator value of PCI obtained on individual samples.

The next step is to choose samples to evaluate. Samples should be distributed evenly over the entire surface of the element. The easiest way to achieve this is to divide the number of all samples through the minimum number, which will give the interval at which the samples should be selected. For example, if the total number of testing samples is 50, and the minimum 16, the interval will be 50/16, which after rounding down gives the value of 3. Thus, if in the study all samples are numbered from 1 to 50, we should take into account the samples with numbers 1, 4, 7, …, 48. On critical elements of functional airports, as a more frequently used circulation ways or intersection, reasonable from the point of view of ensuring the safety and continuity of air operations is to review all samples. This applies in particular to the runways.

The review process itself consists in a list of damage observed on the surface of the pavement, according to the legend contained in the standards. The census of damage is made in tables, noting their type, harmfulness, number and approximate location on the sample. The number of individual damage cases, their harmfulness and density of occurrence are determined for each analyzed sample. Based on the census, the PCI for the individual sample according to the following procedure is determined:

• Calculate distress density as a total area of each distress type to total area of the sample ratio.
• Define the deduct value based on the nomogram for any type of damage and harm corresponding to it (high, medium or low). A typical nomogram is shown in Figure 2 [8].

The maximum allowed number of deducts m is then determined by dependence (2) [8].

m_i = 1 + (9/95)(100 − HDV_i),

(2)

where: HDV_i is the highest, individual deduct value for single sample i.

3.

Determine the largest corrected deduct value, as follows:

• Determining q—number of deducts higher than 5.0;
• Determining total deduct value (the sum of the whole individual deducts);
• Determining the corrected deduct value with q and a total deduct value from correlation curves for airfield pavement (Figure 3);
• Reducing the smallest individual deduct value greater than 5.0 to the value equal to exactly 5.0;
• Repeat steps from a to c until q reaches a value equal to 1.0.

The maximum corrected deduct value is assumed as the highest values, while the PCI indicator value is calculated from Equation (3) [8].

PCI = 100 − CDV_max,

(3)

where CDV is maximum corrected deduct value.

The final value of the PCI for the evaluated element is calculated as the average value of all PCI values obtained on the analyzed samples. In the case where the samples selected for testing have different surface fields, as a final value of the PCI, the weighted average of PCI is considered. As a scale, the surface area of individual samples is taken.

Unfortunately, the PCI is only based on a visual assessment of the surface layer. The basis for the proper estimation of the PCI is to collect reliable and up-to-date data on the pavement condition. It should be emphasized that experts use a visual method, and hence a human factor appears, it can significantly affect the final result of the analysis.

3. Method of the Military University of Technology

Another known approach to technical condition evaluation of airfields functional elements is a methodology developed in the Military University of Technology. With the parameters mentioned in Section 1, it can be used for the pavement condition evaluation and planned maintenance projects.

A damage indicator is calculated depending on the type of airfield pavements. For airfield pavements made in cement concrete technology, it is designated from the following Equation (4) [26,27]:

$$Z_B=\frac{1}{A}\left(1.8·U_1+1.22·U_2+U_3+U_4+U_5\right),$$

(4)

where: Z_B is an index of damage to the airfield pavements made from cement concrete; U₁ is the surface of cracks and felled corners [m²]; U₂ is the surface of cracks and pavement crushes [m²]; U₃ is the surface of the pavement exfoliations [m²]; U₄ is the surface of multiple cracks of pavements [m²]; U₅ is the surface of marl cavities in the pavement surface [m²]; A is a total surface of the pavement [m²].

On the other hand, the index of damage for airfield pavements made in asphalt concrete technology is designated from the following Equation (5) [26,27]:

$$Z_N=\frac{1}{A}\left(B_1+B_2+B_3+B_4+B_5\right),$$

(5)

where: Z_n is an indicator of damage to the airfield pavement from asphalt concrete; B₁ is the surface of blisters [m²]; B₂ is the surface of the chipping or loosening [m²]; B₃ is the surface of the pavement cancers [m²]; B₄ is a surface of heaves or fractures [m²]; B₅ is the surface of longitudinal and transverse cracks [m²]; and A is the total surface of the pavement [m²].

If you need to determine the degree of technical use of the airfield pavement in a given time interval, you can use an indicator calculated from Equation (6) [26,27]:

$$D=Z-Z_p-N,$$

(6)

where: Z is an indicator of damage in a given time unit; Z_P is an indicator of damage that occurred earlier; and N is an indicator of repairs in a given time unit.

On the other hand, the repair rate in a given unit is calculated from the ratio of surface on which repairs were made to the entire surface of the assessed airfield functional element from the following Equation (7) [26,27]:

$$N=\frac{R}{A}·100,$$

(7)

where: R is a repaired surface [m²]; and A is a reference surface (slab or tested area) [m²].

The methodology presented above [26,27] gives a set of classification parameters for the listed indicators, pavement evenness and friction criteria. This set does not include the load capacity parameter due to the fact that the load capacity is a decisive indicator and must be considered separately in the maintenance strategy. The grade of the surface according to the listed operating parameters is presented in

Table 1. A set of parameters qualifying the condition of the airfield’s functional element pavement [27,28].

Pavement Technical Condition	Pavement Class	Damage Indicator Z [%]	Pavement Friction µ [-]	Longitudinal Pavement Evenness RL [mm]	Technical Consumption (Degradation) D [%]		Repair Indicator N [%]
					CC *	AC **
Very good	Class I	Z ≤ 10	µ > 0.57	RL ≤ 4	D < 1.0	D < 1.0	N ≤ 1
Good	Class II	10 < Z ≤ 15	µ > 0.54	4 < RL ≤ 6	1.0 ≤ D ≤ 1.5	1.0 ≤ D ≤ 2.0	1 < N ≤ 2
Medium	Class III	15 < Z ≤ 22	0.50 < µ < 0.54	6 < RL ≤ 8	1.5 < D ≤ 2.0	2.0 < D ≤ 3.0	2 < N ≤ 3
Moderate	Class IV	22 < Z ≤ 25	0.47 < µ < 0.50	8 < RL ≤ 10	2.0 < D ≤ 2.5	3.0 < D ≤ 4.0	3 < N ≤ 4
Bad	Class V	25 < Z ≤ 80	0.43 < µ < 0.47	10 < RL	2.5 < D	4.0 < D	4 < N

* CC cement concrete. ** AC asphalt concrete.

[27,28].

The evaluation of the airfield pavement condition based on the criteria given in Table 1 can be unambiguous if the assessment indicators of individual features will cover. More often, the situation deviates from such a state, and the assessment of the surface of the pavement can be determined in such a case by means of an independent indicator Y [-], calculated therefore from the following Equation (8) [26,27]:

$$\mathrm{Y}=8·Z-6.66·S+0.89·R_L+13.3·D+8.9·N+2.955,$$

(8)

where: Z is an indicator of damage [%]; D is an indicator of technical consumption (degradation) [%]; N is a repair indicator [%]; S is the friction of the surface expressed by the coefficient of friction μ [-]; and R_L is the surface evenness in the longitudinal direction measured with a lath of 4 m length [mm].

The condition of the airfield pavement assessed by means of a general evaluation indicator is classified according to the following scale [26,27]:

• Y ≤ 1.00          – very good,
• 1.00 < Y ≤ 2.00  – good,
• 2.00 < Y ≤ 3.00  – medium,
• 4.00 < Y ≤ 4.00  – poor,
• Y > 4.00          – bad.

4. PSI Method

In the Air Force Institute of Technology, a different method of pavement evaluation was developed, in which the measure of the usage value of airfield pavement is called the Present Serviceability Index calculated from the Equation (9) [27,28]:

$$PSI=5.0-S_w-0.3·\sqrt{Z+N}-0.22·S_p^2,$$

(9)

where: PSI is a present serviceability index [-]; S_w is the average longitudinal evenness measured with a lath of 4.0 m length [cm], S_p is the average transversal evenness measured with a lath of 1.2 m length [cm]; Z is damages indicator [%]; and N is repairs indicator [%].

The serviceability of airfield pavement to perform air operations by aircraft evaluate is as follows:

• PSI ≥ 4.5     – very good,
• 4.5 > PSI ≥ 4.0  – good,
• 4.0 > PSI ≥ 3.5  – fair,
• 3.5 > PSI ≥ 3.0  – sufficient,
• 3.0 > PSI ≥ 2.5  – critical,
• 2.5 > PSI ≥ 2.0  – insufficient,
• 2.0 > PSI ≥ 1.5  – bad,
• PSI < 1.5     – very bad.

The critical condition means that there is a need for maintenance treatments or the repair of the pavement. In the insufficient state of use, the surface may be the reason for damage to jet aircraft, therefore the general overhaul should be considered. A bad condition of the surface means that the surface is not suitable for use and a general refurbishment should be carried out. In the case of a very bad technical surface, the surface is not suitable for use and a thorough reconstruction is necessary.

Attention is drawn to the fact that the value of the Present Serviceability Index was determined based on testing and measurements of the following properties of the airfield pavement:

• The area of damages,
• The area of repaired pavements,
• Longitudinal evenness,
• Transversal roughness.

For pavement condition evaluation, when qualifying for the general renovation, parameters describing the condition of load capacity and friction of the assessed pavement were not taken into account.

Documenting the technical condition is performed in detail and the inventory of the airfield’s functional element pavement is provided. This is a very important process and at the same time a labor-intensive task that requires high knowledge and experience from the expert. Therefore, obtaining a reliable description is a fundamental matter to the reasonable planning of renovation works.

Analyzing the applicable and above characterized approach to the assessment of the airfield’s functional element pavement condition and considering the specificity of the work and the operation process of airfield pavements, the author proposed a new method of comprehensive evaluation of the technical condition of the airfield’s functional element pavement condition, based on the APCI. This indicator takes into account a very wide range of diagnostic parameters, which includes surface damages of pavement, conducted surface repairs and load-bearing indicators, anti-skid properties, evenness and tensile bond strength of surface layer. The following chapter presents the procedure of the proceedings and diagnostic parameters discussed on which depends the value of the selected indicator.

5. Results

Considering the applicable methods of assessing the airport surfaces and their imperfections, the authors have developed a comprehensive method of assessing functional elements of airports based on APCI. The APCI method assumes the assessment of the surface based on its utility parameters. Individual steps of the procedure are presented below.

The presented method was verified for each type of pavement. Mathematical models were checked during tests carried out on civil and military airfields. Data for analysis were collected for over 10 years of research. Some of the most important results can be found in [31] for cement concrete pavements, in [32] for asphalt concrete pavements and in [33] for natural pavements.

5.1. Procedure for Airfield Pavement Condition Evaluation Using the APCI Method

Data obtained from various sources can provide non-uniform information. In particular, those that originate from periodic inspections of surface damages and pavement repairs. The same area tested by one expert can be interpreted in a different way than by another expert. There are many factors such as the perception of a particular man, the degree of his fatigue, lighting the assessed surface, the time of day or even the moisture of the surface. In order to evaluate the pavement in a reliable way, and, to a large extent independent of the human factor, the procedure was unified by developing the APCI method.

The evaluation of the airfield pavements method using the APCI method is based on the results of field research. A wider image of the pavement condition gives the level of its technical condition in which laboratory tests are also taken into account in addition to field research. Field tests include:

• Damages and repairs inventory,
• Testing the pavement load-bearing capacity,
• Testing the pavement anti-skid properties,
• Testing the pavement evenness,
• Testing the tensile bond strength of surface layer.

Whereas, laboratory test include:

• Structural tests,
• Destructive tests—strength tests,
• Durability tests.

The above-mentioned durability tests include a phenomenon of atmospheric and chemical corrosion resulting from a negative impact of patches used for winter maintenance of the airfield functional element surface. Researches study the influence of the atmospheric corrosion factor on the degradation of the airfield functional element pavement [34], which will be included in the process of modelling the APCI in the future.

The basis for the developed methodology is to evaluate individual input parameters based on the results and conducted measurements. When the minimum limit values for even one parameter is not reached, the assessment is interrupted until the repair is carried out. After repairing the area that does not meet the requirements, field and laboratory tests should be carried out again. After obtaining positive test results, the technical condition of the airport surface based on field research and laboratory tests is assessed.

5.2. Input Data for APCI Method

Data delivered to the model developed method come from measurements and field tests and are the results of the degree of degradation, load bearing capacity, anti-skid properties (friction and texture), evenness and tensile bond strength of surface layer. Each of the above-mentioned tests is carried out in a standardized manner, in accordance with the current normative documents.

The evaluation of pavement degradation is carried out on the basis of data obtained during the surface inspection. Experts make inventory of damages and repairs of the surface taking into account the type, number and location of individual damage. The overview is subject to each of the concrete or virtual slab. Its location relative to the element is described with two numbers in the row/band layout. In addition, each slab has its own unique identifier which includes the airfield object symbol in the International Civil Aviation Organization notification, the abbreviated name of the airfield functional element and the location of the slab. In the case of large elements (e.g., runway), the entire hectometer of the surface may be evaluated, which corresponds to a fragment of 100 m length. Damages on the slab occur as spot, linear or surface damage. The legend of damage corresponds to the legend of repairs in which the letter markings are identical to damage. In order to distinguish damages from repairs, repairs are recorded black, while damages are red. The quantities of individual damage and repair are referred to the size of the surface on which they were compiled in an inventory and expressed in units: pcs/m², m/m² or m²/m². For the degradation model, each type of damage or repair enters the characteristic weight determined by experts based on many years of experience. The degradation degree of the airfield functional element is a number of dimensions from 0 to 100, where undamaged surfaces are characterized by a degradation indicator equal to 0, while a completely degraded surface has a degradation indicator equal to 100.

The assessment of the load capacity of the airfield pavement is done by using the ACN-PCN method. The test based on the Polish military standard NO-17-A500: 2016 Airport and road pavements-load capacity testing [35] and method description can be found in [36]. The values of the elastic deflections obtained in the Heavy Weigh Deflectometer device, the structural layer and material parameters of individual layers, including their stiffness (elasticity module) and bending strength, are considered. In addition, the assessment parameter is taken into account directly under the construction of the assessed element. For information on structures and material parameters of individual layers, full diagnosis is performed by core and ground drilling and a soil substrate probing. Bending strength is converted from the obtained results of splitting tensile strength testing (Brazilian method) of cylindrical samples taken from the structural layers of the pavement. Relationship between splitting tensile strength and compressive strength was described in [37]. The result of the surface load capacity assessment is a commonly used PCN and/or number of allowable air operations for an aircraft with an ACN. In the APCI indicator model, the load capacity is taken into account by the load capacity indicator (N), which is determined depending on Equation (10).

$$N=\left(1-\frac{L}{L_p}\right)·100,$$

(10)

where: L is number of allowable air operations obtained in load capacity test [-], and L_p is a designed number of air operations [-].

The evaluation of anti-skid properties is carried out by measuring the coefficient of friction on the airfield pavement based the Polish military standard NO-17-A501: 2015 Airfield pavements—Friction tests [38] and measurement of the surface texture, according to the requirements of standard EN 13036-1: 2010 Road and airfield surface characteristics—Test methods—Part 1: Measurement of pavement surface macrotexture depth using a volumetric patch technique [39] and applicable documents of international air organizations such as: European Union Aviation Safety Agency, International Civil Aviation Organization or Federal Aviation Administration. Both methods were described in [40]. Studies are performed by a device for continuous measurement of the friction coefficient mentioned in the above documents. During the test, the friction coefficient between breaking wheel and the pavement surface is measured. The test is carried out at a speed of 65 km/h or 95 km/h, ensuring the thickness of the water film in front of the measuring wheel with a value of 1 mm. The obtained results refer to tables with requirements contained in the above documents. Requirements relate to a specific measuring device and specific measurement conditions. The test result is a dimensionless number from 0 to 1, where 0 means lack of friction, and 1 means friction force with a value equal to load force. The assessment of the airfield pavement textures is carried out by a volume method by measuring the Mean Texture Depth or the profile method by measuring the Mean Profile Depth. However, these are point methods of the surface texture. Bearing in mind the geometrical dimensions of the airfield functional element, including the most important of them, which is the runway, as well as short time of access to it for operational reasons at every airport, the Airfield Division of the Air Force Institute of Technology designed the innovative measuring system used for measuring anti-skid properties of airfield pavements in a continuous way. It enables a dynamic, simultaneous measurement of the friction coefficient and depth texture of airfield pavement in the trace of measuring tire of the friction tester, in accordance with the applicable regulations. In the APCI model, the result of testing the anti-skid properties is taken into account by an anti-skid indicator derived from the Equation (11), wherein the anti-skid properties indicator is less than 0.4, and its value should be adopted as 100.

$$Wpp=\left(4.6-6.6·W_{ST}\right)·100,$$

(11)

where: W_pp is an anti-skid indicator, and W_ST is an anti-skid properties indicator [-].

Evenness of airfield pavement is assessed on the basis of measurements of inequality, in accordance with the requirements of the Polish military standard NO-17-A502:2015 Airfield pavements—Evenness testing [41], which were described in [42]. The measurement is performed using the planograph, which measures and records clearance values between the theoretical reference line connecting the bottom of the device wheels and the surface at the center point. The measurement is carried out with a frequency every 10 cm with an accuracy of 0.3 mm. The measuring route is divided into sections of 5 m and such samples are subjected to evaluation. The measure of inequality is the imperfection, presented in percents, which means the percentage of samples exceeding the requirements. In the APCI model, the evenness indicator is equal to the imperfection and determines it from the following, Equation (12).

$$R=max\left(R_T;R_L\right),$$

(12)

where: R_T is longitudinal evenness [%], and R_L is transversal evenness [%].

The bond strength of the surface layer of airfield pavement is tested according to standard EN 1542:2000 Products and systems for the protection and repair of concrete structures—Test methods—Measurement of bond strength by pull-off. Measurement of adhesion by detachment [43] included in the Polish military standard NO-17-A204 Airfield concrete pavements—Requirements and test methods for cement concrete pavements [44] and described by [45]. The test is carried out by drilling the surface to a depth of approximately 15 mm with a diameter of 50 mm. Then, the puck with a diameter of 50 mm sticks to the surfaces. When the adhesive is completely bonded, it breaks down using the pull-off apparatus. As a result, the breaking force value is obtained, which, divided by the sample surface, provides the evaluated bond strength. In the APCI model, it is included as the bond strength of the surface layer indicator calculated from the following, Equation (13).

$$Wod=\frac{f_{min}-f_h}{f_{min}}·100,$$

(13)

where: w_od is the bond strength of the surface layer indicator, f_h the bond strength of the surface layer obtained during test [MPa], and f_min is the minimum, required bond strength of the surface layer [MPa].

5.3. Process Analysis

The parameters of the surface collected during field tests are input data for analysis. The weights occurring in the developed model for determining the APCI have been defined by experts, based on years of research and experience of engineers dealing with diagnosing the airfield pavements.

The base for the weight value estimations was the results of multiannual diagnostic tests made on the functional elements of airports located in Poland. Both civil and military objects on which different types of aircraft were operating, were subjected to research. The inspections were carried out on all airfield functional elements, i.e., runways, taxiways and aprons. Airfield pavements of all ages and in various condition, both new and strongly degraded, requiring total reconstruction, were subjected to periodic inspections.

The comprehensive evaluation of the technical condition of the airfield functional element pavement contains the sum of the above-mentioned parameters, standardized and included with specific weights. The weight of individual parameters are decision-making variables and depend on the adopted maintenance strategy. Three types of strategies are distinguished below:

• Priority to improve the structural condition of the surface for which 70% of the combined share of parameters was adopted: the pavement surface condition (degree of degradation) and load capacity (number of the allowed air operations).
• Priority to improve the air traffic safety for which 70% of the total share of parameters was adopted: the pavement surface condition (degree of degradation) and anti-skid properties.
• Priority to minimize the costs of maintenance procedures for which weights are proportional to the unit cost of maintenance works. In this strategy, parameters deciding on the type of maintenance procedure are considered.

Table 2. Weights for various combinations of operating parameters of the airfield’s functional element pavement for calculating the APCI.

Operating Parameters Combination	Degradation	Load Capacity	Anti-Skid Properties	Evenness		Bond Strength
	wD	wN	wWpp	wRL	wRT	wWod
D + N	0.40	0.60
D + N + RL + RT	0.30	0.50		0.10	0.10
D + N + Wpp	0.30	0.55	0.15
D + N + R L	0.30	0.60		0.10
D + N + RL + RT + Wpp + Wod	0.25	0.40	0.15	0.05	0.05	0.10
D + N + RL + RT + Wpp	0.30	0.40	0.15	0.05	0.10
D + N + RL + Wpp + Wod	0.25	0.40	0.15	0.10		0.10

presents the determined values of weights combination for the adopted maintenance strategies of airfield functional element.

5.4. Output Data

The airfield pavements are evaluated according to the criteria for assessing the technical condition of the pavement, based on the APCI values obtained. The criteria were developed on the basis of multiannual field research.

In order to determine the scale of the values of the pavement condition indicators, seven categories of the technical condition of the airfield functional element pavement were introduced. The entire range of the selected variable, from the minimum to maximum, is divided into a certain number of compartments with an even length. These cases for which the values of the selected variable belong to one interval, consist of a common category. In a simplified scale, there are three decision-making levels describing the technical condition of the airfield functional element. A class determining the pavement condition was assigned to each level. The first one is the desirable level, which includes new, renewed and operated surfaces. It is predicted that over the next five years, these surfaces will not require planned renovation works. The warning level, indirect, identifies the pavement condition as such in which it is justified to perform detailed testing towards performing this status. The latest is a critical level, determining the immediate performance of technical and consumables tests to determine the procedures to improve the pavement condition. Figure 4 shows the process of assessing the technical condition of the airfield functional element pavement based on the designated APCI together with criteria.

The APCI scale can be presented in a simplified way as:

• Appropriate (APCI = 71 ÷ 100),
• Degraded (APCI = 56 ÷ 70),
• Unsatisfactory (APCI = 0 ÷ 55).

The critical value of the APCI is the value, after reaching which the technical condition of the pavement leads to rapid deterioration.

Table 3. Criteria for assessing the technical condition of airfield’s functional element pavements based on the APCI.

Condition	APCIG	APCIF	APCIwG	APCIwF	APCI
Good	87 ÷ 100	86 ÷ 100	87 ÷ 100	86 ÷ 100	86 ÷ 100
Satisfactory	73 ÷ 86	70 ÷ 85	72 ÷ 86	72 ÷ 85	71 ÷ 85
Adequate	59 ÷ 72	54 ÷ 69	58 ÷ 71	57 ÷ 71	56 ÷ 70
Poor	45 ÷ 58	39 ÷ 53	43 ÷ 57	42 ÷ 56	41 ÷ 55
Very poor	30 ÷ 44	23 ÷ 38	29 ÷ 42	28 ÷ 41	27 ÷ 40
Serious	16 ÷ 29	8 ÷ 22	14 ÷ 28	13 ÷ 27	12 ÷ 26
Unfit	0 ÷ 15	0 ÷ 7	0 ÷ 13	0 ÷ 12	0 ÷ 11

shows the determined values of the APCI based on the calculated unloaded and weighted indicators by limit values and occupied area. The given values of the APCI should be used in the process of comprehensive assessment of the technical condition of the airfield’s functional element pavement made in cement and asphalt concrete technology.

In APCI^G, APCI^F, APCI^wG and APCI^wF columns present criteria for the value of the APCI index designated on the basis of weighted or non-loaded indicators determined by the surface or limit values methods. The general assessment criteria for the technical condition of the airfield’s functional element pavement based on the APCI was adopted as an average value from the above variants.

5.5. Airfield Cement, Asphalt Concrete Pavement Condition Indicator

The APCI of the airfield pavement made in the cement as well as asphalt concrete technology describes Equation (14). The presented model takes into account the results of periodic inspections, which include damage and repair of airfield’s functional element pavements, load capacity, anti-skid properties, evenness and bond strength of the surface layer.

$$APCI=100-\frac{w_{D}D+w_{N}N+w_{Wpp}Wpp+w_{R}R+w_{Wod}Wod}{\sum w_i}$$

(14)

where: w_D is a weight of the degradation degree of the cement concrete pavement; D is a degradation degree of cement concrete pavement; w_N is a weight of the load capacity of the cement concrete pavement; N is an allowed number of air operations for cement concrete pavement; w_wpp is a weight of anti-skid properties of cement concrete pavement; W_pp is an anti-skid indicator of cement concrete pavement, w_R is a weight of evenness of cement concrete pavement; R is an evenness of cement concrete pavement; w_Wod is a weight of bond strength of surface layer of cement concrete pavement; Wod is a bond strength of surface layer of cement concrete pavement; and $\sum w_i$ is a weighted sum.

5.6. Natural Airfield Pavement Condition Indicator

The assessment of the condition of natural airfields pavements by APCI method is based on the results of field research, which include:

• Tests of turf layer strength to 0.3 m depth,
• Test of the load bearing capacity of natural pavement to 0.85 m depth.

The basis for the developed methodology is to evaluate individual input parameters based on the results and conducted measurements. Research performed in the fields are input data for process analysis, as a result of which the output data describes the condition of the natural airfield pavements. The evaluation procedure is presented in Figure 5.

Data delivered to the model of developed method come from measurements and field tests and are the results of the turf layer strength and the load bearing capacity of the natural pavement, carried out in accordance with the requirements of the Polish military standard NO-17-A503:2017 [46]. The method was also described in [47]. The turf layer test is performed using a turf penetrometer to a depth of 0.3 m. For each measurement point, the average strength of the evaluated layer σ is calculated. On the other hand, testing of the load capacity of natural pavement is performed using a dynamic cone penetrometer to a depth of 0.85 m. The load capacity parameter of the airfield pavement is expressed by the California Bearing Ratio.

The pavement parameters collected during field studies are input data for analysis. The developed model of the APCI_NN for natural airfield pavements presents the following dependence (15).

$$APC{I}_{NN}=100-\frac{\left(w_{\sigma }\sigma +w_{CBR}{W}_{CBR}\right)}{\sum w_i},$$

(15)

where: w_σ is a weight for the strength of a turf layer of a natural pavement; σ is the strength of the turf layer of natural pavement; w_CBR is a weight for the load capacity of the natural pavement; the W_CBR is a load capacity indicator of the average airfield’s functional element natural pavement; and $\sum w_i$ is the sum of weights.

The average load capacity indicator of natural pavement is based on individual results of California Bearing Ratio values designated in an intermediate layer in between 0 ÷ 15 cm and in a layer in between 15 ÷ 85 cm. It is calculated on the basis of the geometrical average of the partial load capacity of load capacity for intermediate layer 0÷15 cm indicator and the partial load capacity of load capacity for intermediate layer 15÷85 cm indicator according to dependence (16).

$$W_{CBR}=\frac{1}{n}{\displaystyle \sum }_{i=1}^n\sqrt{W_{CBR15i}\times W_{CBR85i}},$$

(16)

where: W_CBR15i is a partial indicator of natural pavement load capacity for intermediate layer 0 ÷ 15 cm in i point of tests; W_CBR85i is a partial indicator of natural pavement load capacity for intermediate layer 15 ÷ 85 cm in i point of tests; and n is a number of tests points.

Partial load-bearing indicators are determined using the nomogram presented in Figure 6. The nomogram was created based on rich experience in diagnosing the airfield pavements. For each California Bearing Ratio value, the value of the corresponding indicator is read from the vertical axis, depending on the consumed intermediate layer of natural pavement. The nomogram can vary your character depending on the safety coefficient (W_B) that was adopted. The safety coefficient of 1.2 is assumed as standard. Increasing its values causes lower partial indicators, and thus, tightening the requirements. California Bearing Ratio results in the range of (15W_B, + ∞) each time it gives a partial indicator of 1.0.

Comprehensive assessment of the technical condition of natural airfield’s functional element pavements includes the sum of the above-mentioned parameters, suggested according to standardization and loaded with specific weights. The weight of individual parameters are decision-making variables and depend on the adopted maintenance strategy. There are three types of strategies:

• Priority to improve the condition of the structural condition of natural pavement for which 100% of the total share of parameters was adopted: the turf layer strength and load capacity.
• Priority to improve the balance of air traffic for which 90% of the share of the load capacity was adopted.
• Priority to minimize the costs of maintenance procedures for which weights are proportional to the unit costs of maintenance works. In this strategy, parameters decide on the type of maintenance procedure.

Table 4. Weights for various combinations of operational parameters of natural pavement of airfield functional element for calculating the APCI_NN.

Combination of Natural Pavement Parameters	σ	CBR
σ	0.1
CBR		0.9
σ + CBR	0.1	0.9

presents the determined values of weights for the adopted maintenance strategies of the natural pavement of airfield functional element.

The weights occurring in the developed model for determining the APCI_NN have been defined by experts, as discussed in the earlier methodology. The basis for estimating the values of the weights were the results of multiannual diagnostic tests made on elements of functional elements of civil and military airports located in Poland. The inspections were carried out on all airfield functional elements with natural pavements, i.e.,: Runways, Runway End Safety Areas and Side Runway Shoulders. Periodic inspections are conducted on the natural airfield pavements of all ages and in various conditions of turfs.

Natural airfield pavements are classified according to the criteria for assessing their technical condition, based on the obtained values of the APCI_NN. The criteria were developed on the basis of multiannual field research.

In order to determine the scale of the values of the natural pavement condition indicators, three categories of assessing their technical condition have been introduced. The entire range of the selected variable, from the minimum to maximum, is divided into a certain number of compartments with an even length. These cases for which the values of the selected variable belong to one interval, consist of a common category. Levels determining the pavement condition were assigned to each category. The first category is a good level that includes natural, renewed and operated surfaces that in the next five years will not require planned renovation works. The second category is a sufficient, indirect level that identifies the pavement condition as such in which it is justified to perform detailed testing aiming at treatments to improve the condition of the natural capacity. Additionally, the third category is a bad level, determining the immediate execution of load-bearing tests to determine treatments that improve the condition of the load capacity of the natural surface.

Table 5. Criteria for assessing the technical condition of the natural pavement of airfield functional element.

Condition	APCINN	Definition
Good	71 ÷ 100	The pavement is in good technical condition, has a small or no damage and requires only routine maintenance works.
Satisfactory	38 ÷ 70	The pavement is in a sufficient technical condition, has damage with low and medium harm. In a short time, routine and more serious repairs should be carried out.
Bad	0 ÷ 37	The pavement is in poor technical condition, has damage to high harmfulness that causes operational problems. Maintenance work should include immediate repairs and reconstructions.

presents the assessment criteria for the technical condition of the airfield functional element with natural pavement, based on a designated APCI_NN along with the interpretations of the surface category.

The critical value of the APCI_NN is such a value, after which reaching the technical condition of the natural airfield pavement leads to rapid deterioration.

Table 6. Criteria for assessing the technical condition of natural pavements of airfield functional element based on the APCI_NN.

Condition	APCIG	APCIF	APCIwG	APCIwF	APCINN
Good	73 ÷ 100	70 ÷ 100	72 ÷ 100	72 ÷ 100	71 ÷ 100
Satisfactory	41 ÷ 72	36 ÷ 69	40 ÷ 71	39 ÷ 71	38 ÷ 70
Bad	0 ÷ 40	0 ÷ 35	0 ÷ 39	0 ÷ 38	0 ÷ 37

shows the designated values of the APCI_NN state. The given values of the APCI_NN should be used in comprehensive assessment of the technical condition of natural pavements of airfield functional elements.

APCI_G, APCI_F, APCI_wG, and APCI_wF columns show criteria for the value of the APCI_NN designated on the basis of weighted or unloaded indicators determined by the surface or limit values. The general assessment criteria for the technical condition of the natural pavements of airfield functional element based on the APCI_NN was adopted as an average value from the above variants. More information about natural pavement condition evaluation is described by the authors in [33].

6. Conclusions

Nowadays, there are many methods and procedures for the evaluation of airfield pavements condition. In the introduction section, a few of them are described. Unfortunately, there are only a few procedures that describe the condition of pavement in a more complex way. The most frequent method used for pavement condition assessment is the PCI procedure. This method is used by, e.g., the U.S. Army Corps of Engineers and is popular among engineering and government institutions. However, it is based only on observed surface damages and skips such parameters as load capacity, anti-skid properties, and evenness or structural properties, which are especially important at the airfields where jet aircrafts operate. Non-reference to the above parameters prevents reliable assessment of the real technical condition of the airfield pavement. The management of airfield pavements should take place in a responsible manner. It should be based on reliable information on the technical condition of the pavement. Only this approach enables rational cost planning and managing of resources.

The main goal of the paper was to present a complex approach to technical condition assessment of airfield pavements made in cement concrete and asphalt concrete, as well as natural pavements which are part of the ground maneuvering field on each airfield object. The authors proposed a new methodology, which gives a reliable tool for airport pavements management. The developed method is based on Airfield Pavement Condition Index and includes parameters that describe not only surface damages but also conducted repairs, load capacity of pavement construction, anti-skid properties, evenness and bond strength of the surface layer. This approach gives a much wider view of actual condition of airfield pavement. Each of the parameters is obtained in a standardized test.

Presented mathematical models enable automation of the pavement assessment process. That can lower costs and speed up the whole evaluation. Moreover, presented weights can be changed in future to fit the method to specific conditions met at specific airfield pavements. In future projects authors, will try to use artificial intelligence to define the weights of model parameters based on test results obtained in years of previous and future research.

Authors conduct advanced works on atmospheric corrosion impact on pavement degradation. The results of works can be used for pavement condition evaluation in the context of its durability. In the future, authors would like to use artificial intelligence for model optimization, in particular, to optimize the accepted weights of each parameter. Data is currently collected for this process.