# Long-Term Monitored Road Degradation Functions as a Tool to Increase Quality of Pavement Design
## 2 Research Methods
### Experimental Approach  
When determining the relationships of degradation functions, it is necessary to know the input data that affect the development of parameters of operational capability and performance that are IRI, rut and pavement bearing capacity. For this reason, in cooperation with the Research Center of the University of Zilina, nondestructive road conditions measurements were carried out. The task of the measurements was to determine the thicknesses of the structural layers of the road, to measure in addition to IRI, rut and the load-bearing capacity, gain knowledge about the subsurface failures of the pavement. Ground-penetrating radar (GPR), laser scanning and falling weight deflectometer technologies were used.  
As can be seen at Figure 3 GPR consists of high-performance multi-channel radar control unit with air coupled 2 GHz horn antenna for asphalt layer thickness (from 0 to 0.75 m under surface) and 400 Mhz antenna for sub-base layer information (from 0 to 4 m under surface). The peak load of falling weight deflectometer KUAB 50 which is presented at Figure 4, it can be varied from the keyboard of the computer, by the choice of height of fall. The peak load range is 15-50 kN at 22 ms rise time (=time from start to peak).  
Figure 3: Laser scanner (**a**); Ground-penetrating radar and video inspection (**b**); Diagnostic vehicle-interior (**c**).  
The measurement procedures and used technologies are designed based on many years of best practices, which are described in multiple studies [46; 47; 48]. It is very positive that despite the relatively high-quality equipment, developments in non-destructive data collection technologies are moving forward. An example is new approaches based on advanced sensor systems and machine learning that are emerging, which could significantly help improving the data collection process and road surface classification through integration. An example of progress is the identification of road surfaces in vehicles by acquiring the generated vibration in the suspension due to tire rolling [49] or the definition of the relationships between the road roughness and the ratios of individual amplitudes in a specific frequency band of the vehicle body acceleration values [50; 51].  
The data were evaluated for every 20 m. In Table 2 examples are presented of measurements in the station every 100 m.  
In Figure 5 it is possible to see the values obtained by measuring the rut and IRI at the station 0 to 250 m, for both driving directions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Direction 1 (from Porubka to Poluvisie)**} & \multicolumn{2}{c}{**Direction 2 (from Poluvisie to Porubka)**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **Road** \\ **Stationing** \\ **(m)** \\ \end{tabular} } & **AC Average** & \
\begin{tabular}{c} **Road** \\ **Characterising** \\ **[mm]** \\ \end{tabular} & **Road** & **AC Average** &
\begin{tabular}{c} **Road** \\ **Characterising** \\ **[mm]** \\ \end{tabular} & **AC Average** \\ \multicolumn{2}{c}{**Stationing**} & **Thickness** & **Stationing** & **Thickness** & **Thickness** \\ \multicolumn{2}{c}{  
Based on the above dependence, it is relatively easy to monitor the development trend of the monitored parameter together with its prognosis. For cases where we are interested in determining the time when the limit values occur, it is necessary to know the forecasts of the development of traffic load.  
Based on the defined magnitude, it was possible to design degradation functions. These confirm the results of several studies. They state that the maximum values of transverse inequalities are achieved mainly in the right wheel path. In addition, higher surface tensions arise in this part, which is caused by the deformations of the road shoulder.