## 2 State of the Art
[37] relied on an APMS software, PAVER 7, to determinate the best restoration method based on the type of damage and, therefore, they used PCI to evaluate the pavement condition. However, the decision-making aspects in the airport context cannot ignore the time variable, as maintenance activities in the flight infrastructures could compromise airport operations. Huge investments are made annually for M&R activities related to the airport infrastructures, which is why the stakeholders are always looking for ways to reach the best solutions that also take into account operational effectiveness [38]. For this reason, [PERSON] and [PERSON] [39] developed a finite-horizon dynamic program to investigate the interplays among maintenance and repair M&R action time, functional interdependence between runways and traffic growth. Results from computational studies reveal the tradeoff between the M&R actions, the delay cost and long-term benefits of significantly improving pavement conditions through reconstructing runways and confirm that if an infrastructure is not properly and timely repaired, repair costs could increase exponentially. [PERSON] et al. [40] integrated life-cycle cost analysis (LCCA) and life cycle assessment (LCA) to develop a method for assessing pavement sustainability. In relation to the indirect/user costs, they considered the airport revenue reduction cost and the airline delay cost as, depending on the duration of each pavement event construction, it is necessary to estimate reduction in airplane operations and loss of daily operating revenue. In this paper, the authors illustrate different M&R optimization strategies, already implemented in a previous study by [PERSON] et al. [15], to apply them to a real case study in an updated version. The proposed approach considers the project level of several sections and investigates different scenarios because, especially in the airport sector, there is no optimal strategy, but several possible solutions that can be undertaken by the operator based on the needs of the airport, aiming to minimize the total maintenance cost ensuring the optimum quality level for each performance measure. In this context, the clustering function provided in the present model helps to optimize the areas in which to predict activities thanks to the identification of work-zones. Using the clustering model is a common practice in the pavement management context. For instance, to reliably build network-level pavement performance prediction models, [PERSON] et al. [41] performed homogeneous clusters using k-means clustering, which is a nonhierarchical clustering algorithm, based on PCI, IRI, age, and traffic volume of the pavement sections. In this paper, clustering is used to identify homogeneous zones in which to perform M&R activities. For the evaluation of the pavement condition, the indicators relating to RL, IRI and PCI are used [42], allowing their performance to be evaluated both from a structural and a functional point of view. The three performance indicators reflect those usually used to investigate the air infrastructures through periodic instrumented surveys, but the same approach could be used regardless of any other performance indicator. The optimization model used for this study is detailed below.