## 5. Data Collection and Processing
The dataset includes data from seven U.S. states--Texas, Arizona, New Jersey, Kansas, Oklahoma, Florida, and Arkansas--highlighting diverse climatic zones essential for asphalt pavement analysis. These states encompass all four primary climatic zones pertinent to pavement performance: Wet Freeze, Wet No-Freeze, Dry Freeze, and Dry No-Freeze. For example, New Jersey and parts of Kansas are categorized under the Wet Freeze zone due to their cold winters and substantial precipitation. Texas and Florida, characterized by high humidity and rainfall without freezing temperatures, exemplify the Wet No-Freeze zone. The Dry Freeze zone is represented by certain areas in Kansas, while Arizona primarily illustrates the Dry No-Freeze zone due to its arid climate. This varied selection of states guarantees that the dataset reflects the diverse climatic conditions throughout the U.S., providing a robust basis for examining the influence of different climates on asphalt pavement performance. This dataset includes information from 19 LTPP sections across 7 states in the United States. The geographic distribution of the LTPP sections analyzed in this study is depicted in Figure 4, providing a visual overview of the areas covered in this analysis. This broad sampling helps ensure that the findings are applicable across diverse environmental conditions and traffic patterns. The model's design features an architecture with six different inputs feeding into the input layer, followed by a hidden layer that contains two neurons and culminates with an output variable tasked with predicting the area affected by fatigue cracking. In the Long-Term Pavement Performance (LTPP) database, fatigue cracking is classified into three categories, reflecting varying levels of distress [28]. The total area of cracking is computed by summing the areas corresponding to all three levels of distress. Figure 3: Box plot of (**a**) normalized input variables (\(-1\) to \(1\)), (**b**) data distribution range without normalization. To assess the relationship between the inputs and the predicted output, a linear regression analysis was conducted, resulting in an R\({}^{2}\) value of 0.583. This coefficient of determination is relatively substantial for a multiple linear regression model, suggesting a moderate level of explanatory power regarding how well the input variables predict the output variable. However, it is acknowledged that the accuracy of the predictions could be enhanced by employing an ANN. ANNs are adept at capturing complex nonlinear relationships that linear regression might miss. The statistical relationship between the input variables and the output, as revealed through multiple linear regression, is detailed in Table 1. Figure 5 presents the multiple linear regression analysis between measured and predicted fatigue cracking. The multiple linear regression model articulated in Equation (2), alongside an R\({}^{2}\) value of 0.583, denotes a moderate degree of correlation, suggesting the model captures the trend but with some variability. This setup provides a foundational understanding that suggests potential improvements in predictive accuracy with the use of more advanced modeling techniques like ANNs. \[\text{FCA}=2.169\times\text{TH}+5.874\times\text{AV}-8.589\times\text{AC}+0.044 \times\text{KE}-1.855\text{TEMP}-804.523\times\text{CAR} \tag{2}\]  
Here  
FCA = Fatigue Cracking Area (m\({}^{2}\));  
TH = Thickness of HMA layer (in);  
AV = Air Void (%);  
AC = Asphalt Content (%);  
KE = Cumulative ESAL (kESAL);  
TEMP = Average Annual Temperature (\({}^{\circ}\)C);  
CAR = Normalized Comprehensive Area Ratio (CAr\({}^{\prime}\)). \begin{table}
\begin{tabular}{c c} \hline \hline
**Multiple R** & 0.764 \\ \hline
**R square** & 0.583 \\ \hline
**Adjusted R square** & 0.575 \\ \hline
**Standard error** & 9.189 \\ \hline
**Observation** & 311 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical Summary of the Multiple Linear Regression Analysis. Figure 4: Distribution of LTPP section selected for this study.