# Airport pavement Maintenance Decision-Making System with Condition Cases Optimization
## 1 Introduction
In this paper, we first examine the underlying reasons for this situation and then aim to develop a decision-making system that can flexibly adapt to the spectrum of budgets. Considering the difficulty of rapid changes in annual budgets for individual airports and the need for equitable budget allocation, our system is designed to address these challenges. Secondly, we seek to improve the decision-making system by enhancing each stage's component model, including evaluation indices, performance curves, cost, effects, and optimization models. Our goal is to elevate the system's theoretical validity and rationality using academic research outcomes while simultaneously meeting the practical needs of airport managers. The key components of pavement management systems are the evaluation of pavement conditions and the decision-making process regarding maintenance methods and timing. The dominant airport pavement evaluation index is the PCI, developed by the United States Corps of Engineers in the 1980s [2]. The FAA utilizes the structural condition index (SCI) as part of the PCI, which is associated with structural deterioration, including rutting and alligator cracking. In addition, functional evaluations encompass longitudinal profile, skid resistance, and the PCI excluding the SCI to assess functional performance [3]. [PERSON] [4] introduced the pavement rehabilitation index (PRI) as a Japanese airport pavement evaluation index, adopting cracking, rutting, and profile as key factors. Delft University of Technology in the Netherlands emphasizes the equal importance of functional and structural pavement performance. They evaluate functional performance using PCI, profile, and skid resistance, and adopt the pavement classification number (PCN) method and remaining life analysis as alternatives for assessing structural performance [5]. Amsterdam Airport, Belgium, and Italy have adopted the PCN method only [6]. The aircraft classification number and pavement classification number system (ACN-PCN system, currently changed to ACR-PCR, where R stands for rating) was developed to assess aircraft operational suitability, explicitly stating that it cannot be used as an evaluation basis for design and maintenance decisions [7]. Pavement performance models have been developed using different modeling methods, including mathematical methods, soft computing methods, and hybrid models combining both [8]. The Markov chain method stands out as one of the most widely used approaches. Its advantage lies in its ability to analyze with limited data. Experts can estimate pavement condition change probabilities based on knowledge and experience [9]. Examples of applying the Markov chain method in pavement management include a 1982 study by [PERSON] et al. on optimal maintenance method determination for Arizona state roads [10]. Traditional methods such as ranking have been used to prioritize decisions for maintenance [11], and the decision tree method was introduced by [PERSON] et al. in 1985 [12]. There are cases applying decision models with multivariate variables [13], and the analytic hierarchy process (AHP) technique, which determines importance through pairwise comparisons [14]. The worst-fit method, prioritizing higher rankings as conditions worsen, is relatively straightforward and intuitive, but has limitations in overall improvement of the entire management target [12]. Since the 2010s, there has been a trend in developing priority models using artificial intelligence technology and GIS-based approaches [15]. When using optimization models, the prevalent approach for airport pavement is to estimate individual damage over time and traffic volume and derive an integrated index, rather than using the former method. Linear programming has been used in pavement management since its introduction in the late 1980s [16] and continues to be employed [17]. Integer linear programming has been predominantly used, offering the advantage of shorter calculation times due to its relatively simple modeling [18; 19; 20].