Study on pre-compaction of pavement graded gravels via imaging technologies, artificial intelligent and numerical simulations

Chonghui Wang, Xiaodong Zhou, Pengfei Liu, Guoyang Lu, Hainian Wang, Markus Oeser

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Pavement compaction cannot be neglected during the motorway manufacture stage because it can determine pavement service quality and durability. Concerning the compaction scenario, the paving compaction is responsible for offering the preliminary strength of the pavement. Ignoring paving compaction quality control can lead to over compaction. This paper introduces an integral system to study and simulate the paving compaction of asphalt motorways in Discrete Element Model two-dimensional (DEM2D). This method includes the whole procedure from aggregate image acquisition database establishment to the DEM2D simulation of paving compaction. To this end, this study fulfils the creation of the aggregate database applied in DEM via the Aggregate Image Measuring System (AIMS) method. In addition, the artificial intelligent (AI) technology called Generative Adversarial Networks (GANs) method is proposed to expand the developed DEM aggregate database. Three different approaches are applied to calibrate the accuracy of the extended database. According to the aggregate database, the pavement paving compaction with different aggregate gradations can be simulated in DEM2D.

Original languageEnglish
Article number128380
JournalConstruction and Building Materials
Volume345
DOIs
Publication statusPublished - 22 Aug 2022

Keywords

  • Aggregate
  • Artificial intelligent
  • Asphalt pavement
  • Deep learning
  • Discrete element
  • Image technology
  • Pavement compaction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)

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