## 3 Results and Discussion
### Keywords and Trending Topics of the Research on Pavement Management
#### 3.2.2 Pavement Performance Models  
The pavement management includes developing models that are able to foresee the pavement performance over time. There is a wide range of pavement performance models, deployed by highway administrations to satisfy their needs. At network level, performance models are employed to estimate the future condition of the road network, and to establish M&R works in both short and long term. In contrast, at project level, performance models help to estimate the best alternative of treatment with the best benefit-cost rate. Models are usually based on field observations at network level, and on mechanistic principles only or with in situ data at project level [77; 78; 99]. For the last 15 years, it can be stated that pavement design, especially in North America, has shifted from empirical approach to mechanistic-empirical (M-E) approach. M-E design upgrades empirical design methods by introducing mechanistic behavior, correlating recorded distresses to applied loads in the road structure [100]. The Mechanistic-Empirical Pavement Design Guide (MEPDG) was produced in 2004, principally thanks to the National Cooperative Highway Research Program (NCHRP) Project 1-37A [101], and subsequently delivered to the American Association of State Highway and Transportation Officials (AASHTO) [102]. The Mechanistic-Empirical Pavement Design Guide (MEPDG) [102] can be deployed for flexible and rigid pavement, and includes the most used pavement structures in North America, incorporating the theoretical analysis to field observations, in order to include the intricate consequences of traffic loading, climatic effects, and material aging on pavement distresses. Nevertheless, MEPDG performance models were developed and calibrated with the Long-Term Pavement Performance (LTPP) data [103] and, therefore, differences can be found when introducing \"local\" inputs, such as climate, material characteristics, traffic patterns or construction and M&R methods. Therefore, suggested performance models should be checked and calibrated with local data, as many states of the United States carried out: North Carolina [104], California [105], Texas [106], and Washington [107]. Hence, apart from being employed for designing durable pavement sections, the MEPDG contributes to the pavement management providing reliable and consistent performance models, calibrated both nationally and locally. A national guideline for local calibration, the Guide for the Local Calibration of the Mechanistic-Empirical Pavement Design Guide [108], derived from NCHRP Project 1-40B [109], received considerable research attention. On this basis, the results from a local validation of rutting data recorded in the PMS of the Ministry of Transportation of Ontario (MTO) showed that a unique optimal solution could not be achieved because rutting models were imprecise [103]. A similar conclusion was reached in another study performed in Ontario [110], demonstrating that no new distress models were better than the existing PMS model of the MTO. Likewise, the verification of the MEPDG model for rutting in local conditions showed that the predictions were higher than observed rutting in Louisiana [111], and statistical analysis was then carried out [112]. In contrast, fatigue and International Roughness Index (IRI) models proposed in the MEPDG were concluded to be applicable in Louisiana [113] and in Tennessee [114]. However, some road administrations cannot calibrate the MEPDG models as required inputs are not available. Hence, [PERSON] et al. [115] identified the significant inputs for an IRI model in Alberta (Canada). Moreover, the Pavement Management Guide [4], published in 2012, proposed the performance models shown in the MEPDG, and expects more accurate models as the models become calibrated in more states and countries. Despite the improvement achieved from upgrading the purely empirical design methods, M-E methods must still deal with some uncertainty when estimating pavement behavior.