Abstract

Aggregate segregation is one of the key defects that impair the performance of concrete at fresh and hardened stages. This study aims to assess the feasibility of using an advanced technique to identify/scan/determine the aggregate segregation of concrete elements and determine radiation shielding properties of the concrete. The gamma-ray computed tomography (γ-CT) technique was employed to study aggregate distribution/dispersion in self-consolidating concrete (SCC). Accordingly, two SCC mixtures with moderate segregation (MS) and high segregation (HS) levels were cast and compared with highly stable SCC with no segregation (NS). The γ-CT scans were located on three different levels of concrete specimens located at the top-level (L1), middle-level (L2), and bottom-level (L3). A Cs-137 (660 keV emission) was used as a gamma-ray source to measure the linear attenuation coefficient per unit chord length at each level. Alternating minimization (AM) algorithm was utilized for cross-sectional image reconstruction based on the attenuation coefficient of the material density and the elements atomic number that represent the materials. A phantom of three Plexiglas layers was used to pre-validate the technique's ability to identify aggregate locations. Moreover, the γ-CT scans of the concrete samples were compared with selected sections using the image processing method. The findings confirmed that the γ-CT technique can determine the level of segregation in the investigated samples. Further, it has been found that the presence of the aggregate enhanced the radiation shielding properties by up to ∼25%. The linear attenuation values were used to quantify the aggregate distribution of the concrete samples.

Department(s)

Civil, Architectural and Environmental Engineering

Second Department

Chemical and Biochemical Engineering

Keywords and Phrases

Aggregate distribution; Gamma-ray CT; Linear attenuation coefficient; Segregation; Shielding properties

International Standard Serial Number (ISSN)

0950-0618

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2023 Elsevier, All rights reserved.

Publication Date

11 Apr 2022

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