Title page for ETD etd-0417102-153001

Type of Document Master's Thesis
Author Pan, Tongyan
Author's Email Address tpan1@lsu.edu
URN etd-0417102-153001
Title Fine Aggregate Characterization Using Digital Image Analysis
Degree Master of Science in Civil Engineering (M.S.C.E.)
Department Civil and Environmental Engineering
Advisory Committee
Advisor Name Title
Linbing Wang Committee Chair
Emir J. Macari Committee Co-Chair
  • correlation
  • uncompacted voids content
  • image processing
Date of Defense 2002-04-12
Availability unrestricted
A comprehensive literature review shows that performance of hot mix asphalt (HMA) is influenced by properties of aggregate. Current situation is that only limited efforts were dedicated to aggregate tests and criteria on aggregate, compared to researches on new binder tests, especially to that of fine aggregate. Superpave (Superior Performing Asphalt Pavement) tests/criteria on aggregate need to reflect those properties that influence performance. Representatives of the aggregate industry and the Superpave Mixture/Aggregate ETG (Expert Task Group) have reached the consensus for the need to improve aggregate tests and criteria as one of the most needed aspects left to complete in the Superpave system.

In this thesis, an alternative method is carried out for this purpose with the help of image facilities, due to its accuracy in quantifying the size, shape and surface property of aggregate particles. In this study, basic image acquisition and processing principles were illustrated, and totally eighteen morphological indices were measured over each of the 2500 particles; Sieve Size was compared with the size of particles and positive correlation demonstrated the feasibility of the image method; besides, analysis of angularity showed that either Method A or Method B of Tests of Uncompacted Void Contents could be adopted for correlation of its results with the measured angularity; as an important component of this study, Tests of Uncompacted Voids Content and Internal Friction Angle were performed and their results are correlated with the angularity, and results from both tests provided excellent correlation with image based indices. This study demonstrates the validity of the digital image method in morphological analysis of fine aggregate.

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