Predictive maintenance is essential in aviation due to rising cost pressures, leveraging sensor data and maintenance logs for improving planning efficiency. Analyzing historical data ensures timely interventions, reducing unplanned downtime and enhancing aircraft reliability. Digital twin applications expand these capabilities, allowing precise monitoring and proactive analyses of aircraft components, tracking stress, fatigue, and health conditions. Accurate load monitoring during ground operations requires integrating actual aircraft trajectories with environmental factors like pavement conditions and weather,
which can pose challenges due to data sparsity, noise, or misalignment. Our study outlines a methodology using sparse ADS-B and geospatial airport data, employing map-matching and filtering techniques for comprehensive trajectory representation and analysis. Additionally, we introduce roughness-specific pavement stochastic modeling to allow load assessment on aircraft structures during ground roll including surface variations and damage patterns. This model precedes a probabilistic fatigue model, aiming to initially diagnose potential structural issues to enable subsequent prediction, and mitigate efforts, thereby enhancing aircraft durability and thus operational safety.