## 2 Literature Review
The newly developed parameter, Normalized Comprehensive Area Ratio (CAr'), demonstrates a strong correlation (R2 = 0.96) with pavement fatigue failure predictions, aiding in more informed decision-making for maintenance and rehabilitation strategies within Project Management Systems (PMSs). The (CAr') is calculated as shown in Equation (1). \[ \begin{split}\text{CAr'}=\Big{(}\frac{1}{1524\times D_{0}\times D _{0}}\Big{)}\times\{203\times\Big{(}\frac{D_{0}+D_{03}}{2}\Big{)}+102\times \Big{(}\frac{D_{03}+D_{03}}{2}\Big{)}+152\times\Big{(}\frac{D_{03}+D_{03}}{2} \Big{)}\\ +153\times\Big{(}\frac{D_{03}\times D_{03}}{2}\Big{)}+304\times \Big{(}\frac{D_{03}+D_{04}}{2}\Big{)}+610\times\Big{(}\frac{D_{03}+D_{03}}{2} \Big{)}\Big{\}}\end{split}  \tag{1}\]  
where D0 is the center deflection, D1524 is the deflection at offset 1524 mm from the load, and D1 is the deflection at offset i (mm) from the load. In order to evaluate how various traffic, environmental, and pavement factors affect the pavement performance, [PERSON] et al. [19] utilized data from the LTPP database and applied various statistical methods. The age of the pavement, base type, cumulative traffic intensity, HMA thickness, temperature, and precipitation were identified as the most critical variables influencing pavement performance. [PERSON] et al., in their study, conducted a survival analysis of asphalt pavements utilizing the LTPP database, and it was found that layer thickness, traffic level, precipitation, and freeze-thaw cycles are the main factors influencing fatigue cracking [20]. [PERSON] et al. [21] identified the base type to be a more influential factor in alligator cracking than the HMA thickness. This study also identified climatic conditions to have a significant say in fatigue cracking in a pavement. Pavements with a low thickness are more prone to bottom-up fatigue cracking [22]. [PERSON] et al. [23] conducted a survival analysis of asphalt pavements and studied the effects of pavement thickness and number of axles, among other variables. Their study demonstrated a clear relationship between increased pavement thickness and improved fatigue service life and the effect of an increase in overweight axles to the equivalent AADTT. Among the environmental factors affecting asphalt pavement, temperature factors are found to be more significant than other factors like precipitation, wind speed, percent sunshine, and groundwater level [24]. In a study conducted, a robust relationship between as-constructed air voids and fatigue cracking could not be established [25]. Artificial Neural Networks (ANNs) are becoming more prevalent in the modeling of pavement characteristics because of their ability to process complex and non-linear relationships among input variables and outputs. This capability renders ANNs particularly effective for predicting, analyzing, and optimizing various facets of pavement performance. Recognizing the value of ANNs in scientific research, scholars globally across all scientific disciplines have adopted ANNs as a key modeling tool. Pavement engineers and researchers are no exception and have significantly benefited from incorporating ANNs into their research and studies. Numerous other researchers have utilized ANN to characterize the nonlinear and complex relationship between various pavement parameters for modeling fatigue cracking in asphalt pavements [26; 27].