# Long-Term Monitored Road Degradation Functions as a Tool to Increase Quality of Pavement Design
## 1 Introduction
### Related Research  
In the process of compiling degradation models, it is necessary to take into account factors such as the possibility of measuring input parameters (including the possibility of objectively evaluating them), verifiability of the model empirically based on physical properties and also local conditions. From this also follows the procedure whether we will proceed using the knowledge of the laws of aging of materials or from the analysis of experimental measurements. Procedures for determining degradation models can be defined as follows through three basic approach groups:  
* based on the physical laws of failure mechanics and these models must be calibrated and verified using the results of laboratory tests and field measurements,
* on the basis of repeated laboratory tests of materials under load and conditions that are very close to the real one,
* on the basis of long-term measurement and evaluation of real roads under the same boundary conditions [13]. Road degradation can be monitored, evaluated and forecasted by changes in one parameter or also by changes in several parameters in the aggregated assessment. It is the availability of parameters and the possibilities for obtaining them, that are one of the conditions for selecting one of the above-mentioned methods for determining degradation models. Within international standards, approaches to the creation of degradation models are divided into those that are created on the basis of experimental measurements in real conditions and on the basis of measurements in laboratory conditions [14]. Among the significant works that arose on the basis of real conditions, it is possible to include example results of the long-term pavement performance study [15]. The program and the results are  
Figure 2: General concept of degradation function of pavement [9]. focused on the collection of road performance data, containing more than 2500 test sections at more than 900 locations. The results serve as a very useful basis for follow-up work, such as the study [16], which instead of who developed a transformed linear regression model between the pavement condition index and IRI indices, or the study [17] where on data from the long-term pavement performance program analyzed various options for road maintenance and rehabilitation. In general, activities related to the acquisition of degradation models are not implemented primarily to replace or substantially improve other predictive functions, with universal applicability. For certain regions and specific traffic conditions, it is necessary to develop individual degradation models and constantly work on their improvement. Among the parameters, which are in the set are condition of the road surface in terms of cracks, the course of transverse tracks, skid resistance and load-bearing capacity of road layers, traffic load, climatic conditions, humidity parameters of the internal structure of the road [18; 19]. At the same time, it is necessary to observe in the background time factors and other serious external influences that may occur. Subsequently, it is at the discretion of the creators and users of pavement management systems which collected, derived or estimated data prefer as inputs, or which parameters they consider crucial for determining the road quality. The studies contained in [20; 21] emphasize the use for the pavement performance International Roughness Index (IRI) parameter. This is despite the fact that they collect various other data with different collect frequency (e.g., pavement crack, road roughness) that can provide more comprehensive assessments. While, for example, the studies described in [22] are based on more complicated approaches including road age, traffic load, cracks, temperature and rut depth. The model below states that IRI increases with an increase in age, rut depth, freezing index and plasticity index, while thicker overlay and base layers result in less IRI.