## 1 Introduction  
Fatigue cracking constitutes one of the predominant forms of deterioration observed in flexible pavements [1]. Fatigue cracking in asphalt pavements, also known as alligator cracking, is a form of structural failure caused by repeated traffic loading on pavements that are inadequately designed, constructed with poor-quality materials, or have deteriorated over time [2, 3]. Figure 1a illustrates the mechanism of fatigue cracking in asphalt pavement layers, and Figure 1b exhibits a distinct interconnected pattern, making it easier to visualize and understand its impact on pavement integrity. This type of distress starts at the bottom of the asphalt layer, gradually appearing on the surface as interconnected cracks that resemble alligator skin. Major contributors to fatigue cracking include overloading, aging of the asphalt binder, and moisture infiltration, which weakens the pavement structure [4]. Effective mitigation involves early detection through regular inspections, timely preventive maintenance like crack sealing, and more robust repairs such as overlays or full-depth replacements. To assess the load-bearing capacity of pavements, the Falling Weight Deflectometer (FWD) is an extensively utilized instrument. This device operates on a non-destructiveevaluation principle, employing a mass that is released onto a load plate situated upon the pavement surface to emulate the loading scenarios characteristic of a dynamic wheel load. Its ability to accurately quantify pavement deflections and replicate vehicular loading conditions establishes it as an essential apparatus for appraising the structural robustness of pavements and predicting their prospective performance [5]. Additionally, improving pavement design to handle actual traffic loads and enhancing drainage can help prevent the occurrence of fatigue cracking, extending the pavement's lifespan and improving road safety. Sensors measure the resulting pavement deflection at various distances from the load plate, forming a deflection basin. The analysis of these data allows engineers to estimate the modulus of elasticity, bearing capacity, and stiffness of the pavement layers. This information is crucial for assessing pavement conditions, aiding in maintenance planning, validating design assumptions, and guiding future research. The FWD test plays a key role in ensuring efficient resource allocation for pavement maintenance and improving pavement design and performance. In recent years, models of Artificial Intelligence (AI) have acquired significant recognition within the domain of pavement engineering due to their ability to analyze large datasets, identify patterns, and improve decision-making processes. At the network level, AI empowers transportation authorities to optimize maintenance schedules, allocate resources with precision, and forecast long-term trends in pavement performance. These insights help in developing proactive maintenance strategies, reducing costs, and extending pavement life. At the project level, models driven by AI augment the precision of pavement condition evaluations, thereby facilitating effective rehabilitation planning. High-accuracy predictions guarantee timely and economically viable interventions, thereby enhancing pavement resilience and the overall sustainability of infrastructure. The integration of AI with traditional engineering methods bridges gaps in predictive modeling, enabling more data-driven and informed decision-making for pavement management. In this study, the Normalized Comprehensive Area Ratio (CAr'), a parameter introduced by [PERSON] et al. [6] to characterize the structural integrity of the asphalt pavement is used alongside other crucial pavement, traffic, and environmental factors to model fatigue cracking in an asphalt pavement using an Artificial Neural Network (ANN). This approach aims to enhance fatigue crack prediction accuracy, supporting better decision-making in pavement maintenance and rehabilitation planning.