Coupling asphalt construction process quality into product quality using data-driven methods

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Downloads (Pure)

Abstract

The long-term quality of the asphalt layer is crucial for maintaining the functionality of roads. Despite extensive research on predicting pavement failure modes and the effect of design and road use on the quality of the asphalt layer, there is limited understanding of how the quality of road construction impacts the long-term quality of asphalt pavement. This paper presents a data-driven approach to studying the impact of construction process quality on the International Roughness Index (IRI) of roads. Two machine learning models (Random Forest and Gated Recurrent Unit) were compared in a case study, with the GRU model (R2 of 0.8284) outperforming the RF model (R2 of 0.5498). Results showed that construction process quality was the third most significant factor affecting IRI.

Original languageEnglish
Title of host publicationProceedings of the 40th International Symposium on Automation and Robotics in Construction, ISARC 2023
EditorsBorja Garcia de Soto, Vicente Gonzalez, Ioannis Brilakis
PublisherInternational Association for Automation and Robotics in Construction (IAARC)
Pages349-356
Number of pages8
ISBN (Electronic)978-0-6458322-0-4
DOIs
Publication statusPublished - 2023
Event40th International Symposium on Automation and Robotics in Construction, ISARC 2023 - Indian Institute of Technology (IIT) Madras, Chennai, India
Duration: 5 Jul 20237 Jul 2023
Conference number: 40

Publication series

NameProceedings of the International Symposium on Automation and Robotics in Construction
ISSN (Electronic)2413-5844

Conference

Conference40th International Symposium on Automation and Robotics in Construction, ISARC 2023
Abbreviated titleISARC
Country/TerritoryIndia
CityChennai
Period5/07/237/07/23

Keywords

  • Asphalt construction
  • construction process quality
  • data-driven methods
  • international roughness index (IRI)
  • machine learning
  • regression
  • 2024 OA procedure

Fingerprint

Dive into the research topics of 'Coupling asphalt construction process quality into product quality using data-driven methods'. Together they form a unique fingerprint.

Cite this