## 5. Data Collection and Processing  
For this research, the data were gathered from the LTPP Info pave database. The primary dataset utilized for the analysis includes the FWD data and the Comprehensive Area Ratio (CAr), which is derived using FWD deflection data obtained from the LTPP database. It is derived from deflections measured at various sensor locations relative to the center of the FWD load plate, providing a comprehensive evaluation of pavement response. This parameter helps to quantify the pavement's structural integrity by analyzing deflection patterns under applied loads. Additional parameters chosen for this study are Thickness (T) in inches, mean Asphalt Content (AC) in the AC layer (% by weight), air void (AV) average calculated air voids in the AC layer (%), Cumulative Traffic Load expressed as KESAL (passing of 1000 standard axle of 18 kips), and Annual Average Temperature (TEMP) in \({}^{\circ}\)C. These parameters were selected to comprehensively cover various factors that influence pavement distress, encompassing traffic characteristics, climatic conditions, and specific pavement properties. The variables in this study are selected based on the standards established by the Long-Term Pavement Performance (LTPP) program to ensure precision and uniformity:  
* **Pavement Thickness:** Defined as the thickness of the asphalt or bituminous layers, measured from core samples obtained at designated test sections. The base and subgrade layers are deliberately excluded. * **Asphalt Content:** Refers to the proportion of asphalt binder by weight within the asphalt mixture. This is determined through extraction tests conducted on samples retrieved from the LTPP database. * **Air Voids:** Represents the percentage of void spaces within the densified asphalt mixture. This is calculated based on the difference between the theoretical maximum specific gravity and the bulk specific gravity of the mixture, which is crucial for assessing compaction quality and long-term performance. * **Cumulative Traffic Load:** Expressed in thousands of Equivalent Single Axle Loads (KESALs), this metric quantifies the total accumulated traffic load over a given period. It is derived from traffic monitoring data collected at each test location and is essential for evaluating pavement degradation and functionality. * **Annual Average Temperature:** Represents the mean temperature over a calendar year at the test site, utilizing data from the extensive climate records maintained by the LTPP program. Figure 3 illustrates the distribution of input variables employed in the predictive model via box plots, thereby offering insights into their variability and statistical properties. Figure 3a delineates the normalized input variables, wherein values are scaled within the range of \(-1\) to \(1\), thereby ensuring uniformity in model training and mitigating the risk of variables with larger magnitudes overshadowing the learning process. This normalization process  
augments comparability among diverse variables, including pavement thickness, air voids, asphalt content, cumulative traffic load (KESAL), temperature, and CAr'. Figure 3b displays the distribution of the raw data for the variables mentioned earlier before normalization, which reflects their inherent ranges. The box plots convey critical statistical indicators, such as median, interquartile range, and potential outliers, which facilitate the evaluation of the dataset's spread and variability. This visualization is paramount for comprehending the distribution of input parameters, identifying data imbalances, and ensuring the model's robustness in forecasting fatigue cracking. The selection of these inputs was informed by an extensive review of the literature focusing on fatigue cracking in asphalt pavements. A total of 311 datapoints were collected from the LTPP database, representing a wide range of climatic zones.