Synchrotron X-ray computed microtomography investigation of a mortar affected by alkali–silica reaction: a quantitative characterization of its microstructural features
- 323 Downloads
Alkali–silica reaction (ASR) is one of the most important weathering processes in cement-based materials. The damages caused by ASR have been qualitatively investigated with a number of different techniques. In this study, we present a procedure to obtain quantitative morphological parameters of the ASR reaction effects using synchrotron X-ray microtomography data. We found three different kinds of voids due to the effect of three different mechanisms: (i) cracks from ASR expansion, (ii) irregular-shaped voids due to the aggregate particles dissolution, and (iii) bubbles due to the cement paste preparation. We were able to separate them using morphological parameters (such as surface/volume ratio and aspect-ratio) calculated for each object, thus obtaining, e.g., volume fractions for each kind of voids. From the orientation data, we also studied if any shape preferred orientation was present in the sample, concerning the fractures network, and we found no appreciable preferred orientation. The new analysis procedure we applied in this study proved to be an effective approach for the quantitative characterization of the effects (cracks and porosity development by aggregate weathering) of the ASR reaction in mortars.
KeywordsOrientation Distribution Function Mortar Sample Weathered Sample Shape Prefer Orientation Shape Prefer Orientation
The authors acknowledge an anonymous reviewer for his comments that helped to improve this manuscript and Prof. P.J. Monteiro for helpful discussion during the preliminary stage of this study.
- 13.Wigum BJ (2006) In: Proceedings of 8th CANMET/ACI International Conference on Recent Advances in Concrete Technology, Montreal, pp 111–128Google Scholar
- 15.Stanton TE (1940) ASCE 66:1781Google Scholar
- 16.Diamond S (1992) Strategic Highway Research Program Report (SHRP-C/UWP-92-601), p 470Google Scholar
- 21.Herman GT (1980) Image reconstruction from projections. Elsevier, New YorkGoogle Scholar
- 22.Abramoff MD, Magelhaes PJ, Ram SJ (2004) Biophoton Int 11:36Google Scholar
- 23.Tomasi C, Manduchi R (1998) In: Sixth International Conference on Computer Vision, New Delhi, pp 839–846Google Scholar
- 27.Randle V, Engler O (2000) Introduction to texture analysis. Gordon and Breach, AmsterdamGoogle Scholar