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Pavement Distress Estimation via Signal on Graph Processing.

Salvatore Bruno1, Stefania Colonnese2, Gaetano Scarano2

  • 1Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.

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|December 11, 2022
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Summary
This summary is machine-generated.

This study introduces a new signal on graph (SoG) model and Bayesian estimator to improve automatic road pavement distress detection. The method enhances accuracy in identifying road failures and unreliable measurements for better maintenance scheduling.

Keywords:
Bayesian estimatorautomated distress evaluation systemspavement condition indexpavement distress detectionpavement management programsignal on graph processing

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Area of Science:

  • Civil Engineering
  • Data Science
  • Signal Processing

Background:

  • Automated data collection and processing are increasingly used for pavement inspection.
  • Accurate road pavement health representation is crucial for maintenance.
  • Existing systems can benefit from improved distress detection.

Purpose of the Study:

  • To present a novel signal on graph (SoG) model for road pavement distresses.
  • To develop a nonlinear Bayesian estimator for distress metric recovery.
  • To enhance automatic pavement distress detection systems.

Main Methods:

  • Development of a signal on graph (SoG) model for pavement distress representation.
  • Derivation of a nonlinear Bayesian estimator for distress metric recovery.
  • Validation using a large dataset from field tests in Kazakhstan.

Main Results:

  • The proposed methodology effectively recovers acquisition errors in pavement data.
  • Improved detection of road failures is achieved.
  • Identification of unreliable 3D laser measurement sections is enabled.

Conclusions:

  • The developed SoG model and Bayesian estimator improve pavement distress detection.
  • The methodology aids in identifying measurement unreliability.
  • The approach supports optimized road maintenance scheduling.