Machine Learning Approach to Identifying Key Environmental Factors for Airfield Asphalt Pavement Performance

Machine learning (ML) techniques are promising methods for developing predictive models involving multiple interrelated predictors. A key step in an ML procedure is feature engineering, which is a method for converting raw data into sets of useful and relevant features that provide the best performance model prediction. In this study, the Federal Aviation Administration (FAA) applied feature engineering to the problem of identifying the key environmental variables (climate and weather) that influence airfield asphalt pavement performance. The FAA implemented various feature selection and feature construction methods based on supervised and unsupervised learning algorithms. Selected environmental variables will become inputs to the machine learning models being developed to predict long-term pavement performance. Data from the FAA extended airport pavement life (EAPL) program were used in this study. The EAPL database includes various pavement performance measures, such as PCI and derivative indexes, surface friction and profile roughness indices, as well as maintenance work histories, historical runway usage, and historical weather data for runways at large- and medium-hub U.S. airports. In this study, the effect of certain environmental variables was evaluated with respect to the performance index anti-SCI, a derivative of the PCI containing only those distresses that are not directly caused by aircraft loads.