Investigation of the permeability of porous concrete reconstructed using probabilistic description methods

Sang Yeop Chung, Tong Seok Han, Se Yun Kim, Tae Hyung Lee

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Porous concrete has a high void ratio with continuously interconnected void clusters. The void distribution in concrete strongly affects its physical properties, such as strength and percolation. To investigate and quantify the spatial distribution of voids inside porous concrete specimens, computed tomography (CT) images and low-order probability functions can be used. In this study, we reconstruct porous concrete specimens with different void distributions using low-order probability functions. The void distributions of the original and reconstructed porous concrete specimens should exhibit almost the same statistical characteristics. We confirm that reconstructed porous concrete specimens generated using the proposed probabilistic optimization process have statistically identical characteristics, and exhibit similar material behaviors as with the original model. These reconstructed specimens can be utilized for numerical experiments so as to reduce the number of time-consuming real experiments.

Original languageEnglish
Pages (from-to)760-770
Number of pages11
JournalConstruction and Building Materials
Volume66
DOIs
Publication statusPublished - 2014 Sep 15

Bibliographical note

Funding Information:
This research was supported by a Korea Research Foundation Grant funded by the Korean Government ( NRF-2011-0029212 and NRF-2012R1A1A2006629 ). This work was also supported by the Industrial Strategic Technology Development Program ( 10041589 ) funded by the Ministry of Knowledge Economy (MKE, Korea). In addition, the authors wish to extend their gratitude to Prof. Young Kug Jo of Chungwoon University, Republic of Korea, who provided porous concrete specimens and experimental data, and to Severance Hospital, Yonsei University, Republic of Korea, for their assistance with CT imaging.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)

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