Modelling Asphalt Overlay As-Built Roughness Based on Profile Transformation-Case for Paver Using Automatic Levelling System.

Sensors (Basel)

Departamento de Ingeniería del Transporte, Territorio y Urbanismo, Universidad Politécnica de Madrid, 28040 Madrid, Spain.

Published: March 2024

AI Article Synopsis

  • The as-built roughness of pavement is crucial for assessing road quality and user experience, making accurate predictions important for effective pavement design and management.
  • A new prediction model for asphalt overlays, originally based on wheeled pavers without automatic control, has been enhanced to include an automatic screed control system using mobile reference data.
  • The model was calibrated using longitudinal profile data from an established pavement performance study, achieving a mean prediction error of 0.17 m/km, showcasing the model’s effectiveness as a promising alternative for pavement roughness estimation.

Article Abstract

The as-built roughness, or smoothness obtained during pavement construction, plays an important role in road engineering since it serves as an indicator for both the level of service provided to users and the overall standard of construction quality. Being able to predict as-built roughness is therefore important for supporting pavement design and management decision making. An as-built IRI prediction model for asphalt overlays based on profile transformation was proposed in a previous study. The model, used as basis for this work, was developed for the case of wheeled pavers without automatic screed levelling. This study presents further development of the base prediction model, including the use of an automatic screed control system through a long-distance averaging mobile reference. Formulation of linear systems that constitute the model are presented for the case of a wheeled paver using contactless acoustic sensors set-up over a floating levelling beam attached to the paver. To calibrate the model, longitudinal profile data from the Long-Term Pavement Performance SPS-5 experiment was used, obtaining a mean error of 0.17 m/km for the predicted IRI. The results obtained demonstrate the potential of the proposed approach as a modelling alternative.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014191PMC
http://dx.doi.org/10.3390/s24072131DOI Listing

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