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Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

Nicholas Fiorentini1,2, Mehdi Maboudi2, Pietro Leandri1

  • 1Department of Civil and Industrial Engineering, Engineering School of the University of Pisa, Largo Lucio Lazzarino 1, 56126 Pisa, Italy.

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Summary

This study correlates Synthetic Aperture Radar (SAR) data with road roughness surveys. Results show SAR can predict pavement condition in some areas, potentially replacing costly in situ road surveys.

Keywords:
International Roughness Index (IRI)Machine Learning Algorithms (MLAs)Pavement Management SystemsPersistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR)laser profilometric surveysradar interferometryroad roughness

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Area of Science:

  • Geosciences
  • Civil Engineering
  • Remote Sensing

Background:

  • Road pavement condition monitoring is crucial for infrastructure management.
  • Exogenous phenomena like subsidence can impact road surfaces.
  • Current methods for assessing road roughness can be time-consuming and expensive.

Purpose of the Study:

  • To develop and validate a methodology for correlating Synthetic Aperture Radar (SAR) measurements with road roughness data.
  • To assess the potential of Machine Learning Algorithms (MLAs) trained on SAR data to predict pavement condition and International Roughness Index (IRI).
  • To determine if SAR-based predictions can replace in situ road surveys for certain road sections.

Main Methods:

  • Utilized Persistent Scatterer Interferometric SAR (PS-InSAR) data over a 964 km² area in Tuscany, Italy.
  • Integrated MLA predictions of vertical displacement with 10 km of in situ profilometric road roughness measurements.
  • Calculated the International Roughness Index (IRI) from in situ data for comparison.

Main Results:

  • A clear association was found between MLA-estimated displacements and IRI values for several road sections.
  • For other road sections, the relationship was weaker, indicating the necessity of in situ surveys.
  • The findings suggest that road regularity is driven by exogenous factors in predictable sections and endogenous factors in others.

Conclusions:

  • SAR-based MLAs show promise for monitoring road conditions and potentially replacing in situ surveys in specific contexts.
  • The effectiveness of MLAs is dependent on the factors influencing road regularity (exogenous vs. endogenous).
  • Future development of MLAs incorporating endogenous factors like traffic and material properties could enhance pavement quality estimation over large networks.