## 3 Results
The snow and wet snow conditions, corresponding to a RWYCC of 3, presented a BPN of 19 \(\pm\) 2 with wet snow and 23 \(\pm\) 2 for snow-only, indicating that the wet snow conditions lead to more slippery states. The wet snow condition was close to the slush condition which corresponded to a RWYCC of 2. Slush represents snow supersaturated with water that should be easily evacuated when compressed with the hand. In our case, the water could not be removed from the snow, unless a high pressure was applied, so the condition was considered as wet snow. The next condition corresponded to ice cover, with a RWYCC of 1, which resulted in a BPN of 17 \(\pm\) 1, indicating that the surface is quite slippery. It corresponded to a reduction of 66% from the reference. [PERSON] et al. (2017) observed a reduction of approximately 70% in their ice conditions. The BPN is even more reduced when the snow is added to the ice layer, corresponding to a RWYCC of 0, and representing the worst condition to be encountered on the runway. It has been confirmed with a BPN of 11 \(\pm\) 2, close to the lower limit of the pendulum. In this case, the dry snow acts as a lubricant on ice, rendering the surface even more slippery and hence more dangerous. In order to validate the results, a linear fitting has been added to the graph. Most of the data were slightly over the curves except for the snow and wet snow conditions which were under. The obtained equation is  
\[\text{BPN}=(5.5\pm 0.7)\text{RWYCC}+(10.2\pm 2.4) \tag{1}\]  
The intercept was not forced to 0 since at the worst condition, a BPN of 11 was still obtainable. The Pearson correlation coefficient (_r_) of 0.96 indicates that the relation is strongly positive, validating the results. Finally, the coefficient of determination (\(R^{2}\)) of 0.93 also indicated that more than 90% of the data fits with this linear assumption. It is obvious that most of the laboratory-reproduced conditions resulted in BPN data which correlated well with the RWYCC. By applying [PERSON] et al. 's formula, it is possible to  
Figure 5: BPN as a function of the GRF RWYCC. estimate the value of the coefficient of friction (\(\mu\)). The empirical relationship is presented in Equation (2) as follows:  
\[\mu=\frac{3\text{BPN}}{(330-\text{BPN})} \tag{2}\]  
The calculated values are presented in the Table 3.  
The CRFI range presented in the GRF document [22] is also presented in Table 2. A criterion of <10% of difference at the targeted range has been applied to verify if the measured values are in the range. All values are within range except for snow and wet snow, which are much lower than the targeted range. If the values were considered with a RWYCC of 2, these results could be considered comparable to the range. This may signify that the simulated conditions are more dangerous than the specified values, indicating that the tests will be more conservative than the reality, adding an additional safety factor. However, the [PERSON]'s equation is based on measurements and may also induce an error greater than 20%. Therefore, the estimated values of \(\mu\) should be only used as a comparison and not as a direct value of \(\mu\). In order to deepen the analysis of this correlation, data were collected in different wet conditions which could be close to those encountered on the runway. Visually, the wet condition should also include the presence of liquid runway de-icing products in their initial concentration and also in their diluted concentration. So, in addition to the wet condition with water at 4 \({}^{\circ}\)C, data were collected when the concrete was wetted by KFO, KAC and HYB RDPs at \(-5\) \({}^{\circ}\)C. It is not unusual for aircraft to encounter a runway completely wetted by RDPs. It is the case that when the RDP is applied in anti-icing mode, before precipitation, and also when RDPs melt the icy contaminants.