## 9 Discussion  
The present study demonstrates originality by developing a prediction model for fatigue cracking in asphalt pavements using Artificial Neural Networks (ANNs) combined with pavement parameters derived from Falling Weight Deflectometer (FWD) measurements, including the Car' parameter, pavement thickness, cumulative traffic load, and climatic factors. The model, trained on 311 datapoints collected from 19 Long-Term Pavement Performance (LTPP) sections across seven states in the United States, achieved a robust predictive accuracy (R\({}^{2}\) = 0.90). By directly integrating ANN modeling techniques with real-world field-measured data, this approach facilitates proactive pavement management and offers simplified equations suitable for practical engineering applications. In comparison, [PERSON] et al. [35] focused primarily on Bayesian frameworks emphasizing uncertainty quantification rather than practical predictive tools for fatigue cracking. Meanwhile, [PERSON] et al. [4] explored fatigue cracking through controlled laboratory experiments, focusing explicitly on the influence of aging and moisture damage through dynamic modulus and fatigue tests, without integrating ANN-based predictive models or field-measured FWD parameters. Although this study model shows strong predictive capabilities, further improvements in robustness could be achieved by expanding the dataset to include additional LTPP sections from more diverse climatic regions, as previous research indicates significant variability in fatigue cracking behavior under different environmental and traffic conditions. This expanded dataset would enhance the model's generalization and predictive reliability, thus increasing its practical relevance and effectiveness for pavement professionals. Additionally, this study utilized FWD data from a single drop height, whereas the existing research suggests that considering multiple drop heights can provide a more comprehensive understanding of pavement deflection behavior. Future studies should incorporate FWD data from various drop heights to create a more versatile and adaptive predictive model. Comparing this approach to previous fatigue cracking models, researchers have found that including multi-depth deflection measurements enhances model accuracy by capturing the pavement response more effectively under varying stress conditions. Furthermore, while this study identified asphalt content as the most influential factor in fatigue cracking predictions consistent with prior research, some studies highlight the impact of moisture susceptibility, binder aging, and traffic loading as equally or more significant under certain conditions [4,36]. Opposing viewpoints argue that environmental factors, such as freeze-thaw cycles and drainage efficiency, can play a dominant role in long-term pavement durability. This underscores the need for further investigations to identify additional key variables that influence fatigue cracking across different pavement structures and climatic zones. Future models should integrate these additional influential parameters to enhance predictive accuracy. The sensitivity analysis of the AC thickness showed very minimal influence in the fatigue crack area in contrast with the available literature, this may be remedied in future studies by including more diverse datasets. A promising direction for future research is the integration of advanced technologies such as image processing and real-time sensor data acquisition. By aligning image processing algorithms with the existing database, pavement condition assessments can be automated and improved in real time. Recent advancements in computer vision and deep learning have demonstrated the feasibility of detecting fatigue cracks using camera and sensor modules. Implementing these techniques would enable the remote monitoring of pavement conditions, allowing maintenance agencies to receive real-time fatigue crack predictions wirelessly [37].