# Using a Random Forest Model to Predict the Location of Potential Damage on Asphalt Pavement
## 3 Data Collection and Preparation
### Data Collection and Preparation  
The data related to deterioration were collected and arranged in Table 1. Thirty-four variables were chosen to build the forest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Symbol** & **Unit** \\ \hline The original rutting depth & R0 & mm \\ The rutting depth on the fifth day & R5 & mm \\ The rutting depth on the tenth day & R10 & mm \\ The rutting depth on the fifteenth day & R15 & mm \\ The rutting depth on the twentieth day & R20 & mm \\ Increase in rutting depth at the first stage of the test & RI1 & mm \\ Increase in rutting depth in the second stage of the test & RI2 & mm \\ Increase in rutting depth in the third stage of the test & RI3 & mm \\ Increase in rutting depth in the fourth stage of the test & RI4 & mm \\ Increase in rutting depth through the whole test & RI20 & mm \\ The original deflection value & D0 & 0.001 mm \\ The deflection value on the fifth day & D5 & 0.001 mm \\ The deflection value on the tenth day & D10 & 0.001 mm \\ The deflection value on the fifteenth day & D15 & 0.001 mm \\ The deflection value on the twentieth day & D20 & 0.001 mm \\ Increase in the deflection value in the first stage of the test & DI1 & 0.001 mm \\ Increase in the deflection value in the second stage of the test & DI2 & 0.001 mm \\ Increase in the deflection value in the fourth stage of the test & DI4 & 0.001 mm \\ Increase in the deflection value through the whole test & DI20 & 0.001 mm \\ The original IRI value & IRI0 & m/km \\ The IRI value on the fifth day & IRI5 & m/km \\ The IRI value on the tenth day & IRI10 & m/km \\ The IRI value on the fifteenth day & IRI15 & m/km \\ The IRI value on the twentieth day & IRI20 & m/km \\ Increase in the IRI value in the first stage of the test & IRI11 & m/km \\ Increase in the IRI value in the second stage of the test & IRI22 & m/km \\ Increase in the IRI value in the third stage of the test & IRI3 & m/km \\ Increase in the IRI value in the fourth stage of the test & IRI4 & m/km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of variables.  
Figure 1: The structure of the test road.  
The data can be separated into two groups, which are initial data before the test running and in-process data during the test. Some variables imply that damage has already occurred. However, when a decision was made to uncover the surface, it was found that the result was not accurate. Some variables are linear with predictions that can be accumulated by different weights, but some variables are non-linear whose margins are hard to decide. That is why an RF model is needed to improve the accuracy of predictions.  
The data are collected as much as detected to avoid disregarding any small factor, which may also have influence on the predicting result. Nevertheless, some of the variables are dependent on other inputs. We prefer more information richness than data independence. This is because the RF model is very good at multicollinear problems. Besides, if there are some negative factors introduced in the model, they can be pruned in the procedure of model optimization for computing spend and model strength.