Advanced image analysis and techniques for degradation characterization of aggregates

Moaveni, Maziar

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https://hdl.handle.net/2142/78734 Copy

Description

Title

Advanced image analysis and techniques for degradation characterization of aggregates

Author(s)

Moaveni, Maziar

Issue Date

2015-04-23

Director of Research (if dissertation) or Advisor (if thesis)

Tutumluer, Erol

Doctoral Committee Chair(s)

Tutumluer, Erol

Committee Member(s)

• Thompson, Marshall R.
• Barkan, Christopher P.L.
• Roesler, Jeffery R.
• Mahmoud, Enad

Department of Study

Civil & Environmental Eng

Discipline

Civil Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Aggregate shape properties
• Image processing
• Aggregate degradation
• Machine vision
• Image segmentation
• Reclaimed Asphalt Pavement (RAP)

Abstract

Morphological or shape properties of virgin and recycled aggregate sources are known to affect pavement and railroad track mechanistic behavior and performance significantly in terms of strength, modulus and permanent deformation. Under repeated traffic loading aggregate particles used in construction of pavement and railroad track are routinely subjected to degradation through attrition, impact, grinding and polishing type mechanisms, which result in altering their shape and size properties. The recent advances in digital image acquisition and processing techniques have the potential to be used for objective and accurate measurement of aggregate particle size and shape properties in a rapid, reliable and automated fashion both in the laboratory and in the field. The primary focus of this dissertation includes the design, manufacturing, calibration and validation of different hardware and software components of an Enhanced-University of Illinois Aggregate Image Analyzer (E-UIAIA) with many improvements over the first generation device. A new fully automated color image segmentation algorithm was developed as part of this research which showed excellent performance in detecting aggregate particles with different sizes and natural colors. Customized Look Up Tables (LUTs) were developed to enhance the Hue (H) and Saturation (S) representations of dark and bright aggregate images which improved the thresholding results. The different binary image processing modules available in the original UIAIA device for computing size and shape properties of aggregate particles were updated and merged into a single user friendly interface. Moreover, a new processing algorithm for image arithmetic operations and thresholding was developed and validated for computing the percentages of asphalt coating on Reclaimed Asphalt Pavement (RAP) aggregates. The research findings presented in this dissertation include the implementation of newly developed E-UIAIA in capturing the rate and magnitude of changes in shape and size properties of aggregate particles caused by abrasion, polishing and breakage actions at different degradation levels. The standard laboratory degradation test results including Los Angeles Abrasion (LAA) and Micro-Deval (MD) were combined with imaging based particle shape indices to successfully classify different aggregate sources according to their resistance to degradation. As a step forward for bringing the advances in aggregate imaging methods to project sites and quarries, this dissertation introduces a field aggregate image acquisition and processing procedure. Advanced image analysis and segmentation techniques that combine a Markov Random Field (MRF) approach for image modeling, graph cut for optimization and user interaction for enforcing hard constraints were used. The developed algorithm was utilized for extraction and analyses of individual aggregate particle size and shape properties from 2D field images of multi-aggregate particles captured in a single frame using a Digital Single Lens Reflex (DSLR) camera. The developed field imaging and segmentation methodology showed satisfactory performance in two case studies involving quantification of size and shape properties of large size aggregate sources as well as railroad ballast samples collected from various ballast depths in a mainline freight railroad track. The image acquisition and processing methodologies presented in this dissertation hold the potential to provide optimized aggregate resource selection, better aggregate quality control and quality assurance (QC/QA) as well as improved material specifications.

Graduation Semester

2015-5

Type of Resource

text

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Copyright and License Information

Copyright 2015 Maziar Moaveni

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