## 10 Conclusions  
This study highlights the advanced application of the FWD data in conjunction with ANNs to enhance the predictive accuracy of fatigue cracking in asphalt pavements. The FWD data, crucial for this analysis, provide detailed insights into the structural integrity of pavements by measuring pavement deflections under simulated traffic loads. These data form the basis for calculating the CAr', which alongside other factors, serves as a critical input to the ANN model. The model performance was validated through the R\({}^{2}\) value of 0.90. Furthermore, this paper validates the use of FWD data in the form of CAr' in conjunction with other pavement parameters as important factors in predicting fatigue cracking.  
This study meticulously applies sensitivity analysis to ascertain the influence of each input variable on the model's output, which is vital for prioritizing maintenance and design adjustments. The sensitivity indices from the analysis demonstrate that, apart from asphalt content, factors like air voids and average annual temperature also significantly impact the model's predictions, albeit to a lesser extent. This nuanced understanding allows engineers to address the most impactful factors and adjust pavement design specifications more precisely to mitigate fatigue cracking.  
Furthermore, this paper discusses the potential of ANNs in overcoming the limitations of traditional linear models. ANNs excel in handling nonlinear relationships and complex interactions between multiple variables, making them especially suitable for modeling dynamic systems like pavement conditions under varying traffic and environmental conditions. The predictive model developed through this research is intended not only to identify current pavement conditions but also to forecast future deterioration, allowing for proactive pavement management. This predictive capability is crucial for optimizing maintenance schedules, extending pavement life, and reducing repair costs.  
Lastly, this study aims to simplify the complex ANN model into a more user-friendly equation that practitioners can easily employ without needing extensive computational resources. This effort to democratize advanced modeling techniques speaks to the practical implications of the research, aiming to make sophisticated analytical tools accessible for widespread use in pavement management and planning. This aspect of this study underscores its relevance to real-world applications, providing a bridge between high-level research and everyday engineering practices. Despite the promising results, this study has several challenges and limitations. The applicability of the model is similarly constrained by variances in geography and climate, given that it was formulated utilizing data extracted from particular locales, thus necessitating additional investigation for more extensive adaptation.  
Conceptualization, B.K. and M.I.; methodology, B.K.; software, B.K.; validation, B.K., S.P., M.I. and M.S.; formal analysis, B.K. and S.P.; data curation, B.K.; writing--original draft preparation, B.K. and S.P.; writing--review and editing, M.I., B.K. and S.P.; visualization, B.K.; supervision, M.I. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://infopave.fhwa.dot.gov/](https://infopave.fhwa.dot.gov/)]([https://infopave.fhwa.dot.gov/](https://infopave.fhwa.dot.gov/)), accessed on 31 August 2024].  
**Acknowledgments:** We would like to acknowledge OpenAI and the associated APIs for their assistance with language refinement and grammatical corrections during the preparation of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.