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Image-based Restoration of the Concrete Void System Using 2D-to-3D Unfolding Technique
: Received: 6 November 2020 / Approved: 8 November 2020 / Online: 8 November 2020 (14:53:32 CET)
A peer-reviewed article of this Preprint also exists.
Journal reference: Construction and Building Materials 2020
Hardened air void analysis provides essential information of concrete freeze-thaw durability based on the size and spacing of air voids in the material. As the physical freeze-thaw experiment is time-consuming and costly, the characteristics of concrete air voids are often deemed as a proxy of the freeze-thaw performance. This analysis is typically done by measuring the 2D air void intersections on polished samples, but the current interpretation of the 2D void characters does not accurately represent the actual void structure in 3D. To solve this problem, a 2D-to-3D unfolding technique has been proposed in the field of stereology. However, the unfolding analysis is known to be sensitive to several factors, such as void population and size along with a binning scheme, where improper unfolding can considerably bias the prediction of the actual concrete void system. This study investigates the optimal strategy of conducting the unfolding analysis for concrete. The investigation is carried out on both idealized void systems to interrogate the influence of the critical factors individually, and real concrete samples with varying levels of air entrainment to assess the concrete-specific impacts. The concrete void system is studied based on a stereological model emulating the intersected 3D air voids on the surface of polished concrete. The results highlight that, for unfolding concrete voids, logarithmic binning scheme is far more accurate to linear binning. The low unfolding error of the concrete samples indicates that the proposed methodology enables an accurate restoration of 3D void size distribution.
air void analysis; freeze-thaw; flatbed scanner; stereology; unfolding; 3D void analysis; void size distribution; lognormal distribution; logarithmic binning
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