## 4 Conclusions  
A proven technique for enhancing road safety is the use of High Friction Surface Treatments (HFSTs), which increase pavement friction, especially in high-risk locations like curves and intersections. For efficient design and performance tracking, the skid resistance of various treatments must be quantified. A standardized metric for assessing pavement friction based on surface texture characteristics and friction measurements is the IFI, as specified by ASTM E1960. The IFI procedure, on the other hand, is predicated on the idea that pavement friction changes substantially with slip speed during testing, with distinct components, representing the contributions of microtexture and macrotexture, respectively, accounting for friction at low and high speeds. For many typical pavements, this approach works well, but it might not be sufficient to describe the friction behavior of other surface types. Friction levels in HFST and certain ribbed pavements are often less susceptible to changes in slip speed since they stay mostly constant throughout a range of slip speeds. This study aims to evaluate alternative data-driven models for predicting the IFI in HFST using laboratory-measured COF and MPD. The DFT result demonstrated sensitivity to a variety of factors. CB consistently had higher COF values across all sizes, both before and after polishing. Rhy had lower COF values, implying that it may lose friction more quickly with extended polishing than CB, which retains frictional properties better. Both aggregates, of varying sizes, demonstrated texture reduction between initial values and measurements after 140K polishing cycles. During the first 30K cycles, some samples' textures increased slightly before decreasing. This behavior can be attributed to the wear of loosely bound particles and angular but weak particles caused by the TWPD tires' mechanical polishing force. All three models performed well in fitting experimental data, with R\({}^{2}\) values ranging from 0.80 to 0.82 on the testing dataset. The logarithmic model had the highest R\({}^{2}\) (0.821) and a low RMSE (0.049), indicating high capability in the experimental range. The power law model had an R\({}^{2}\) of 0.813 and an RMSE of 0.050, whereas the polynomial model had an R\({}^{2}\) of 0.802 and an RMSE of 0.052. The surface response plots revealed varying degrees of curvature, implying different sensitivities to the COF and MPD parameters. According to the sensitivity analysis, with \(\Delta\)IFI ranging from 14.3% to 17.7% for a \(\pm\)20% change in COF compared to 1.5% to 6.0% for MPD, anticipated IFI values are more impacted by COF than MPD. While the logarithmic and power law models had lower and more consistent sensitivity (-14-15%), the polynomial model was the most sensitive to both inputs. The findings enable a relevant laboratory evaluation of HFST friction resistance, which is required for informed material selection and design optimization. By giving a validated method for determining IFI using laboratory measures. The presented models (especially logarithmic) provide an indispensable instrument for predicting IFI of HFSTs using field-measurable input parameters (COF at 20 km/h and MPD). Future work will incorporate field validation on in-service HFST sites employing DFT + CTM testing to compare lab-predicted IFI to field performance recommendations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **COF (Max \(\Delta\)IFI, \%)** & **MPD (Max \(\Delta\)IFI, \%)** \\ \hline Logarithmic & 14.3 & 1.8 \\ Power law & 14.7 & 1.5 \\ Polynomial & 17.7 & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Maximum relative change in IFI (\(\Delta\)IFI) for \(\pm\)20% variation in COF and MPD. **Author Contributions:** Conceptualization, A.R. and M.A. ; methodology, A.R. and M.A. ; software, A.R. ; validation, A.R. and M.A. ; formal analysis, A.R. ; investigation, A.R. ; resources, M.A. ; data curation, A.R. and M.A. ; writing--original draft preparation, A.R. ; writing--review and editing, M.A. ; visualization, A.R.