## 3 Results and Discussion
Source: CC Report (m/px). Figure 11 represents a plan view of the model. Once the surface feature was assigned, it was possible to use tools that allow you to plot longitudinal axes, raise longitudinal profiles, draw corridors, among other options related to analysis and drawing of paths. Figure 12 shows the longitudinal axes associated with the right girdle and left girdle of the road, respectively, and then the longitudinal profiles corresponding to each axis were obtained. Based on the axes drawn, the longitudinal profiles corresponding to each axis were obtained. A minimum spacing or interval of 1 cm was considered for each point corresponding to the profile elevation. Figure 13 shows the longitudinal profiles obtained. In conjunction with the profiles, a report was obtained containing the list of dimensions and spacing associated with each of them. This report was used as input for the calculation of IRI. Replicating the mathematical concept of the MERLIN team, an Excel spreadsheet was generated, which allows for the estimation of IRI values. To do this, 1-m spacing between dimensions was considered, given the geometric characteristics of the equipment. A histogram was built, and the sample range \"D\" was obtained and then used corresponding mathematical correlations for this class 3 profilometer. The longitudinal profiles considered for the calculation of the IRI through the simulation of the Car Room were subjected to a filtering process in ProVAL. The moving average low-pass (MALP) filter was used for this purpose. A filter cut-off value was used for waves equal to or less than 8.3 m. This filter seeks to smooth the longitudinal profile of the point-to-point model, taking into account the possible differences in height that could occur in the vicinity of the distance of 1 (cm) between point and point. The value of 8.3 (m) used is the best value for our model and resolution that corrects errors and gradients originated from the reconstruction and triangulation of photographs and that do not correspond to the reality in the field. Figure 11: Surface mapping. Figure 12: Longitudinal path of the right axis (**a**) and left axis (**b**). Table 1 then summarizes the results obtained after the proposed experiments and methods are completed comparing the reference value obtained with the Profilometer MLP with our proposed alternative methods. Table 4 shows that the results obtained through the alternative application of the MERLIN and Quarter Car method are close to the reference results and in no case exceed them. In terms of the error between the results and the reference IRI, Table 5 shows that in the application of the MERLIN method, an error of 45% was obtained in the right sash, and a zero error was obtained for the left sash. The quarter car method presents a more uniform error for each girdle, making this method preferable to the MERLIN. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Axis**} & **IRI Profilometer MLP (m/km) –** & **IRI MERLIN** & **IRI QUARTER CAR** \\  & **(Reference Value)** & **(m/km)** & **(m/km)** \\ \hline Right sash & 2.2 & 1.2 & 2.1 \\ Left sash & 1.7 & 1.7 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: International roughness index (IRI) results obtained according to proposed methods. Figure 13: Longitudinal profiles for left and right sashes. The lower the IRI values, the lower the road's roughness; therefore, the road has a better texture and comfort for driving. The results obtained underestimate the real roughness of the road, so they are optimistic about the real situation. Therefore, our proposal's application must be applied with caution; it is not yet recommended to certify a new road. However, the method could be a good tool for self-control or monitoring of roads in operation.