3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates - Phase 2

Riprap rock and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering to armor shorelines, streambeds, bridge abutments, pilings and other shoreline structures against scour and water or ice erosion. They provide erosion control, sediment control, and scour protection. The sustainable and reliable use of riprap materials demands efficient and accurate evaluation of their large particle sizes, shapes, and gradation information at both quarry production lines and construction sites. The objective of this research project is to develop an advanced non-intrusive machine vision system for field evaluation of riprap aggregates, whereby engineers can obtain 3D size and shape information of individual particles in a riprap stockpile simply by taking videos/images with mobile devices such as smartphone camera.

Language

• English

Project

• Status: Active
• Funding: $390000
• Contract Numbers:

  R27-214

• Sponsor Organizations:

  Illinois Department of Transportation

  Bureau of Materials and Physical Research
  126 East Ash Street
  Springfield, IL  United States  62704-4766
• Managing Organizations:

  University of Illinois, Urbana-Champaign

  Illinois Center for Transportation
  1611 Titan Drive
  Rantoul, IL  United States  61866
• Principal Investigators:

  Tutumluer, Erol

• Start Date: 20200701
• Expected Completion Date: 20220630
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Aggregates; Image analysis; Machine learning; Riprap; Shape; Size; Stockpiling
• Subject Areas: Data and Information Technology; Highways; Materials; Pavements;

Filing Info

• Accession Number: 01744076
• Record Type: Research project
• Source Agency: Illinois Department of Transportation
• Contract Numbers: R27-214
• Files: RIP, STATEDOT
• Created Date: Jun 25 2020 10:21AM