Airport asphalt pavement health monitoring system for mechanical model updating and distress evaluation under realistic random aircraft loads

The performance of airport asphalt pavement gradually deteriorates under aircraft load repetitions. In this study, a framework of airport asphalt pavement health monitoring system was developed to evaluate pavement performance under realistic random aircraft loads. The taxiway of the Beijing Capital International Airport was used as an example, and sensor layout was designed to obtain pavement mechanical responses and identify random aircraft loads. Multi-layered system theory was selected as the mechanical model to simulate airport asphalt pavement structure considering its computational efficiency. Mechanical model updating and distress evaluation based on airport asphalt pavement health monitoring system were discussed. First, in accordance with the theoretical relationship between mechanical responses and moduli derived from the mechanical model, the moduli of the pavement structure layers were back-calculated using measured mechanical responses. The Kalman filtering method was developed to identify modulus parameters that can be used to update the mechanical model. Second, full-field pavement mechanical responses under each load were reconstructed from the mechanical responses of a single point acquired by the sensors based on the updated mechanical model with knowledge of random aircraft loads and modulus parameters. Finally, real-time deterministic pavement distress calculation (cumulative damage from fatigue cracking and rutting) at any point were realized based on transfer functions between mechanical responses and performance. The strategy and process for short-term non-deterministic distress prediction were developed, and non-deterministic pavement distress at any point after multiple random aircraft loads can then be obtained. This study is beneficial for the understanding of airport pavement behavior and decision-making regarding pavement maintenance. (C) 2019 Elsevier Ltd. All rights reserved.