## 3 Overviews of the Articles Published in the Special Issue
### Damage Assessment by the PCI  
[PERSON] et al. [Contribution 1] examined the applicability of pavement overlays using life cycle cost (LCC) analysis. Firstly, they analyzed the number of lanes, traffic volume, and shoulder availability on South Korean highways. Then, the Construction Analysis for Pavement Rehabilitation Strategies (CA4 PRS) software was used to convert the value per vehicle on roads with traffic blockages due to overlay construction into user cost. There was a reduction in the user cost when the shoulder was installed. The pavement construction costs were determined, and all of the overlay methods were identified from the existing literature. Lastly, LCC analysis was conducted by applying the user costs, construction costs, and life cycles of pavement overlays. [PERSON] et al. [Contribution 2] introduced the existing PCI procedure and used the findings to redefine the main types of distress found from airport pavements in S. Korea. Airports mainly use pavement management indices such as the FOD index, using the PCI for safety issues. These indices are utilized in decision-making systems for ordinary pavement maintenance and repair. However, since the most significant airports in S. Korea are mid-sized or large and commercial, the authors pointed out problems related to the direct applications of the deduction value of the existing PCI. In addition, a deduction value curve considering the severity level of the main distress types and a corrected deduction value curve for multiple distress types regarding the standard pavement form were prepared using panel rating. Lastly, a new KPCI was recommended with the proposed curves, and the test field results were compared with the current PCI values to investigate the applicability of the KPCI. [PERSON] et al. [Contribution 3] presented a new methodology that can aid in decision making related to management strategies through a more efficient and systematic analysis of the vast annual pavement condition database. The traditional method, polynomial regression analysis, which is calculated through relatively complex formulae, is applicable only at the network level of pavement management. The new approach introduced in this study employs \"particle filtering,\" a machine learning method, to allow for a hybrid-style application at the network and project levels. Additionally, this method aims to capture the characteristics of time series data more accurately. For training, this study utilized time series pavement condition data from 469 individual unit segments (10 m) at various ages of (19, 20, 21, 23, 25, 27, 29, and 30) years. The minimum conditions required to ensure at least 90% prediction accuracy were determined as follows: (1) determining the initial pavement condition index--deciding the basic model form; (2) determining the appropriate number of particles--15,000; and (3) determining the minimum number of time series points--five consecutive pavement condition survey data points for the same segment. [PERSON] et al. [Contribution 6] evaluated the level of functional damage on the pavement surface using the UV method, which is one of the NDT techniques. The findings were used to suggest a method to determine the depth of the pavement damage. It was observed that the currently used management method does not adequately detect the deteriorated pavement area at early ages. From the UV test results, the sound rating of the concrete pavement was graded. Also, a decreasing tendency of UV penetration was confirmed as the propagation depth of the deteriorated areas increases. [PERSON] et al. [Contribution 8] presented an extensive case study of the evaluation of distress observed in pavement propped up on a mechanically stabilized earth (MSE) wall that showed a significant degree of distress. Extensive testing, including visual observations, FWD tests, coring operations, and various field tests, revealed the primary causes of distress. A critical insight derived from this study is the role of backfill soils in embankment stability.