## 2 Literature Review  
The first APMS was established in the 1970s by the Civil Engineering Research Laboratory (CERL) for the U.S. Air Force, leading to the creation of the Pavement Maintenance Management System (PAVER) and the MicroPAVER system [4]. Later, the Federal Aviation Administration (FAA) published Advisory Circular No. 150/5380-6/1983, providing guidelines for maintaining airport pavements, with additional documents released to optimize pavement management from both technical and economic perspectives and which was distributed to international airports [5]. In the United States, APMS is widely used for monitoring airport pavements, with the Pavement Condition Index (PCI) being assessed regularly to guide maintenance plans [6; 7; 8; 9].  
In Europe, practices vary by airport. For example, London Heathrow updates PCI annually and conducts a Pavement Classification Number (PCN) test every decade, while Amsterdam Schiphol and Brussels Airport perform PCI assessments biennially and every three years, respectively, also periodically using PCN tests [10]. Distress identification is a key component of safety risk management (SRM), helping mitigate potential hazards (distresses) that impact aviation safety [11]. Regular pavement condition assessments, such as PCI, friction, and irregularity measurements, are crucial for effective maintenance and repair [12].  
Various combined measurement systems have been developed to objectively assess pavement condition. The engineering assessment procedure, for example, integrates the PCI, structural index (ACN/PCN), friction properties, and foreign object damage (FOD) potential to provide a comprehensive evaluation of airport pavements [12]. Additionally, indices like the Pavement Rehabilitation Index (PRI), developed from over 20 years of data in Japan, and the Condition Rating Index (CRI) in Canada, which uses factors such as the Distress Manifestation Index (DMI) and skid number (SN), help guide maintenance decisions [13; 14].  
Recent advancements include machine learning models, such as those developed by [PERSON] et al., that estimate the Structural Condition Index (SCI) and FOD using algorithms like ensemble learning, CatBoost, and LightGBM, applied to data from numerous airports across the US [15]. Performance assessments using cost-based methods and clustering techniques have also been used to optimize pavement maintenance strategies [16]. Studies by [PERSON] and [PERSON] have revealed relationships between roughness, friction, and FOD, contributing to the development of serviceability level (SL) composite indices for pavement evaluation [17].  
Further research has explored innovative techniques for assessing pavement condition, including wavelet-based pavement deterioration classification [18], expert systems [19], decision trees [20], multilayer system theory [21], complex signal analysis methods [22], confusion regression models [23], as well as advanced approaches like drone-mounted imaging for automated distress detection using convolutional neural networks and deep learning [24; 25].  
The integration of APMS and SRM has also been successfully demonstrated in highway pavement management, with studies examining the impact of pavement properties on road safety and performance, such as those by [PERSON] et al. and [PERSON] [26; 27]. Additionally, combined measurements serve as a communication tool for senior managers, elected officials, and the public, while providing an overall quality index that informs technical and engineering decisions for pavement management at the project level [28].