Abstract Asphaltic airport pavements are difficult to maintain because they are often damaged by the cyclical loads from aircraft and specialized maintenance services vehicles as well as local climatic conditions (Chen et al., 2019; Horonjeff et al., 2010; Sulejmani et al., 2020). During winter, the use of de-icing agents to restore pavement skid resistance may also cause chemical damage to the pavement surface and produces Foreign Object Debris (FOD). The asphalt binders at the pavement surface are also susceptible to oxidative aging when they are exposed to high temperatures and cyclical heat and moisture (An et al., 2021; Kandhal and Chakraborty, 1996; Lu et al., 201 5; Omranian et al., 2020). Hence, airport pavements require systemic inspection at planned intervals and timely repairs for aircraft safety. At busy airports, pavement repairs are usually carried out during off-peak hours to minimize disruption to the aircraft maneuvering areas. Because of the limited repair window, it may not be possible to rectify some non-critical pavement distress within the available time. Currently, repairs are prioritized according to the severity of pavement distress using a simple rating scale (FAA, 2014a), and the repair hours are estimated based on experience or expert judgments. The accuracy of such assessments depends on the traffic environment, the repair technologies, and improvements in pavement design and materials. Hence, this paper proposes an improved method to forecast pavement repair hours that uses technical change, pavement condition, aircraft traffic, and rainfall, including their lagged values, as independent variables. Technical change is modeled as an exogenous variable that changes over time. Because of endogeneity, the structural feedback equations are estimated using the Vector-Autoregressive (VAR) modeling method (Floyd, 2005; Triacca, 2014; Trinh, 2018). The model is tested using five years of monthly data (60 observations per variable) collected at Changi Airport for asphaltic airport pavements in tropical environments. The major findings are that technical change, the lagged value of pavement condition, and the change in aircraft traffic do not explain why the repair hours are increasing over time. The main determinants of repair hours are rainfall and previous repair hours. Since the monsoon periods in Singapore coincide with the holiday seasons, more pavement distress is likely to occur due to the combined damage of traffic loads and the presence of excessive moisture.

    Highlights Vector autoregressive (VAR) modeling method is used to predict airport pavement repair hours. The main determinants of airport pavement repair hours are rainfall and previous repair hours. More pavement distress is found likely to occur during the monsoon periods. Repair hours appear not affected by technical change, pavement condition, and aircraft traffic. The proposed model is tested using five years of real airport data in tropical environments.


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    Titel :

    Forecasting airport pavement repair hours


    Beteiligte:
    Yeung, Calvin (Autor:in) / Tan, Willie (Autor:in)


    Erscheinungsdatum :

    2022-08-05




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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