Highlights Close-Proximity measurements assess rolling noise on road surfaces currently. More frequent opportunistic sound and vibration observations in trunk of vehicles are proposed. Calibration methodology is needed to deal with uncertainty. De-noising Autoencoder improves convergence and eliminates confounders.

    Abstract Currently, municipalities assess rolling noise on road surfaces using Close-Proximity measurements (CPX). To avoid these labor-intensive measurements, an opportunistic approach based on commodity sensors in a fleet of cars, is proposed. Blind sensor calibration eliminates the effect of measurement vehicle and varying observation conditions. Calibration relies on spatial coherence: modifiers and confounders do not interact strongly with location while the quantity of interest depends on location and less on measurement vehicle. Generalized additive speed models, car offset and de-noising autoencoders (DAE) were investigated. DAE achieves prominent results: (1) ratio of variability of measurements at a single location to the variability of measurements over all locations increases, (2) convergence of mean measurement at a location is faster, and (3) seasonal effects are eliminated. Finally, although the proposed method includes a diversity of tires, below 1600 Hz its results differ from CPX less than the difference between bi-annually repeated CPX measurements.


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    Titel :

    Opportunistic monitoring of pavements for noise labeling and mitigation with machine learning


    Beteiligte:


    Erscheinungsdatum :

    2020-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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