The general feature for studying the predicted performance as well as the historical data is a graphical screen giving all the performance curves.

An example is given in figure 3 for a specific road section.

Figure 3: Historical and predicted performance for a road section.

The present year is 2004, and the graphs show the performance data for the road section for the years 2000 to 2023 based on the measured conditions indicated by the yellow squares, and the models entered for the pavement layers.

The upper left graph indicates the loss in bearing capacity due to decreasing moduli.
The values are expressed as the amount of overlay required to reproduce the initial bearing capacity from year 2000.

It is seen that a cut off value to the models is met in year 2019, as defined by the user.

From the rut depth graph and the friction graph it is seen that the models respects the measured data in year 2003, where they “resets” the input to the incremental-recursive procedure.

It is also possible to let the graphics show the modelling without respecting the measured data.
This will highlight the difference between the model prediction and measured data, and is a useful feature for calibrating the models.

The input tables for the sectional pavement structure and surface condition data is shown in figure 4:

Figure 4: Input tables for the sectional pavement structure and surface condition data

Each set of data is related to the year of testing, and additional columns with new test data can be entered over time.

The pavement structure is defined by a number of layers with a material name, layer thickness and modulus.

The material name refers to the database of materials as defined by the user, and where PERS® will read all the necessary information about performance models used in the analysis.

An example for the unbound material type “G1” can be seen from figure 5: In this case the constant A for the modulus model is entered as 0, meaning that no deterioration of the elastic modulus is considered. This setup of materials forms a part of a complete parameter setup. It is possible to define a range of different parameter setups to be used with different part of the road network. That makes it possible to define different models for different climatic regions, or to define different tables of rehabilitation alternatives to be used with different road classes.

Figure 5: Input screen for materials and performance models.

The other parts are setup of rehabilitation alternatives, user cost models and critical limits to the conditions, all used with the effect/cost analysis.

The effect/cost analysis can be run automatically for a user-defined group of sections connected to a specific parameter setup.