# Long-Term Monitored Road Degradation Functions as a Tool to Increase Quality of Pavement Design
## 2 Research Methods  
### Theoretical Approach  
Experimental approaches for determining the course of road aging differ significantly, despite certain common principles. The baseline curve of the road degrading without intervention is expressed by a decreasing curve [27; 28; 29]. Over the last 20 years, a number of scientific approaches have been presented to predict road performance and residual life. They were based on the evaluation of long-term measured data in the statistical form [30], stochastic approaches using probabilistic trends from real measurements [31; 32] or fuzzy logic and analytic hierarchy process [33], or the artificial neural network approach [34]. However, among the most reliable approaches, it is possible to include those based on a combination of long-term collected data, combined with high quality diagnostic and evaluation equipment [35]. This approach represents a backward regression in the form of an iteration between traffic conditions, the specific construction of the road and the condition of its surface [2; 36; 37]. The experimental approach presented in the article is based on monitoring the technical condition of a specific section of Slovak 1-st class road over a period of 20 years, while this monitoring continues. This is an important 1 st class road (built in 1973) marked as I/64 connecting the transport hub of northern Slovakia (the city of Zilina) to another regional center (the city of Prievidza). Specifically, the monitored section is located between Poluvsie and Porubka cities, in a zone where relatively regular and sudden fluctuations in temperature and changes in weather conditions occur during the year. The annual average temperature is about 7.5 \({}^{\circ}\)C, while the warmest month of July has an average temperature of 16.7 \({}^{\circ}\)C. The average number of days with a snow cover is about 50 days. The average height of the snow cover is 15 cm and during dry winters only 5 cm. The maximum height of the snow cover is 70 cm. The road construction consists of a music asphalt concrete, an asphalt concrete for the load-bearing layer, an asphalt concrete for the upper base layer, a cement-bonded mixture and an unbonded layer of gravel. Daily intensity is at the level of about 12,600 vehicles, while the share of heavy trucks is about 16%. \begin{table}
\begin{tabular}{c c c} \hline
**Country** & **Evaluation Function** & **Explanation of Function** \\ \hline \multicolumn{3}{c}{**Overview of Approaches for Pavement Bearing Capacity Calculation**} \\ \hline \multirow{3}{*}{Hungary} & \(E=a-b\times AGE\) & \(AGE\) is age of the road abrasive layer, \\  & \(E=a-b\times FORG\) & FORG is number of repeated crossings of equivalent \\  & \(E=a-b\times FORG\) & passage cars \\  & \(a\), \(b\) are constants \\ \hline \multicolumn{3}{c}{**Other Approach—Structural Failure Index**} \\ \hline \multirow{3}{*}{Spain} & \(ID\) is structural failure index \\  & \(ND\) is the number of vehicle \\  & \(a\), \(b\) are the constants determined from the tables according \\  & \(to\) the road construction \\ \hline \multicolumn{3}{c}{**Other Approach—Surface Failure Index**} \\ \hline \multirow{3}{*}{Finland} & \(DI_{p}\) is predicted failure index \\  & \(DI_{m}\) is failure index-measured, \\  & \(AGE_{om}\) is time period in years from the last replacement of \\  & \(the\) cover to the year of measurement \\  & \(AGE_{ob}\) is time period in years from the last cover \\  & replacement to the year of prediction, \\  & \(b\) is constant based on type of pavement \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.