A successful pavement management system requires an accurate pavement performance prediction model. A novel pavement performance model using the piecewise approximation approach was developed to estimate the pavement serviceable life. It can be broadly applied to estimate pavement performance of any distress types or indexes. The basic theory of the piecewise approximation is to divide the whole pavement serviceable life into three zones: Zone 1 for early age pavement distress, Zone 2 in rehabilitation stage, and Zone 3 for overdistressed situations. Historical pavement performance data are regressed independently in each time zone. This approach can accurately predict pavement distress progression trends in each individual zone by eliminating possible impacts from biased data in other zones. This paper describes the theoretical piecewise approximation process of data classification and model regression and then demonstrates an implementation for a group of Washington State Department of Transportation asphalt concrete pavements. The results are compared with the Mechanistic–Empirical Pavement Design Guide incremental damage approach, the current Washington State Pavement Management System (WSPMS) exponential model, and ordinary regression on all data points. Results indicate that the proposed approach is able to estimate the most accurate rehabilitation due year and to predict the performance trends for each divided zone. The piecewise approximation approach is planned for implementation into the WSPMS and will play an important role in decision making for future pavement rehabilitations.


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    Titel :

    Pavement Performance Modeling Using Piecewise Approximation


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Li, Jianhua (Autor:in) / Luhr, David R. (Autor:in) / Uhlmeyer, Jeff S. (Autor:in)


    Erscheinungsdatum :

    01.01.2010




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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