# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 1 Introduction  
Concrete pavements are the main structure type for airport runways; however, with the approach to their designed service life and the rapid increase in air traffic volumes, the runway pavement performance deteriorates. Composite pavements are a special pavement structure type used for runway rehabilitation, where a hot mix asphalt (HMA) concrete overlay is paved onto the initial surface layer of the Portland cement concrete (PCC) pavement. Composite pavements have been widely adopted in airport runways constructed since the 1990s [1]. However, due to the modulus difference between PCC and HMA, composite pavements exhibit different distress features from both concrete pavements and asphalt pavements [2,3]. Thus, the timely and pertinent preventive maintenance of runway composite pavements is vital for improving the current performance or decelerating the performance deterioration rate so as to delay the demand for the next major repair or rehabilitation. Because of limited maintenance funds, it is necessary to make long-term preventive maintenance decisions that are economical and efficient. Long-term preventive maintenance aims to recover the surface performance before sharp deterioration occurs, which can contribute to the minimization of the total maintenance costs [4,5]. This maintenance strategy combines several maintenance methods to suit various annual maintenancefunds. An effective long-term preventive maintenance strategy includes determining the runway region to be maintained, selecting the optimal maintenance time [6; 7], and deciding the optimal maintenance method [8; 9]. Accurate performance prediction for composite pavements is the prerequisite for conducting maintenance decisions [10]. Many scholars have developed prediction models to quantify the tendency for performance deterioration. [PERSON] et al. [11] developed distress prediction models for composite pavements in Louisiana based on the fundamental concept of pavement rate of deterioration as a function of age, which provided good prediction capabilities for assessing pavement deterioration. [PERSON] et al. [12] developed performance prediction models for composite pavements in Iowa based on statistics and artificial intelligence (AI) techniques, and the prediction accuracy was validated at the project and network level. [PERSON] et al. [13] predict the performance of eight long-term pavement performance (LTPP) composite pavement sections using layer thickness, material properties, traffic volumes, climatic data, and national calibration prediction models. [PERSON] et al. [3] developed transverse cracking and longitudinal cracking performance prediction models for overlay treatment of composite pavements in Louisiana, considering the influence factors that included equivalent single axle load (\(ESAL\)), thickness of composite pavement structural layers, temperature indexes, etc. These researches focus on predicting the performance of road or highway composite pavements and show feasibility in these fields, while few prediction methods have been conducted on airport runway composite pavements, which present different structural and traffic conditions. After predicting the pavement performance tendency, suitable preventive maintenance at a proper time is needed to prevent pavement performance from sharp deterioration. The linear programming method aiming to minimize total costs was first used to conduct pavement maintenance decision-making [14]. With the objective of minimizing the total maintenance cost while meeting the demand of pavement performance threshold, the mixed-integer programming model was also developed [15]. [PERSON] et al. [16] proposed a decision-making process to select appropriate maintenance treatments for reflective cracking in composite pavements. Selecting the optimal maintenance method and the optimal time is actually a multi-objective optimization problem, which aims to acquire the greatest improvement in pavement condition using as few maintenance funds as possible [17; 18; 19].