Modulus is a critical parameter for evaluating the bearing capacity and remaining life of pavements. The prevalent modulus back-calculation method by using the pavement surface deflections captured from falling weight deflectometer (FWD) could characterize the overall bearing capacity of pavements. However, a non-uniqueness issue may occur when identifying the modulus of each layer from the perspective of mathematics. Alternatively, this paper presents a novel application to real-time modulus evaluation based on asphalt pavement health monitoring with built-in sensors. First, the sensor layout is optimized on the basis of the theoretical relationship between modulus and mechanical response inside the pavement. The proposed sensor layout, including input (random loads), output (mechanical responses), and environmental sensing modules, is illustrated in detail. Then, the procedure for real-time modulus evaluation is proposed, and the convergence and uniqueness of modulus evaluation results are investigated. Lastly, a case study of the realistic asphalt pavement health-monitoring system is conducted to demonstrate the effectiveness of the application to real-time modulus evaluation. The results indicate that the proposed modulus evaluation method has a potential to identify the modulus of each pavement structural layer in real time with a convergent and unique solution under a realistic traffic load. The evaluation process has the advantage of not interfering with traffic compared with the prevalent FWD-based modulus evaluation method. The pavement health monitoring with built-in sensors is recommended for newly built roads to facilitate long-term real-time performance assessment and maintenance decision making.


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    Titel :

    Toward Asphalt Pavement Health Monitoring With Built-In Sensors: A Novel Application to Real-Time Modulus Evaluation


    Beteiligte:
    Ma, Xianyong (Autor:in) / Dong, Zejiao (Autor:in) / Dong, Yongkang (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-11-01


    Format / Umfang :

    6257625 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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