scholarly journals Characterization of Porous Cementitious Materials Using Microscopic Image Processing and X-ray CT Analysis

Materials ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3105
Author(s):  
Jinyoung Yoon ◽  
Hyunjun Kim ◽  
Sung-Han Sim ◽  
Sukhoon Pyo

The use of lightweight concrete has continuously increased because it has a primary benefit of reducing dead load in a concrete infrastructure. Various properties of lightweight concrete, such as compressive strength, elastic modulus, sound absorption performance, and thermal insulation, are highly related to its pore characteristics. Consequently, the identification of the characteristics of its pores is an important task. This study performs a comparative analysis for characterizing the pores in cementitious materials using three different testing methods: a water absorption test, microscopic image processing, and X-ray computed tomography (X-ray CT) analysis. For all 12 porous cementitious materials, conventional water absorption test was conducted to obtain their water permeable porosities. Using the microscopic image processing method, various characteristics of pores were identified in terms of the 2D pore ratio (i.e., ratio of pore area to total surface area), the pore size, and the number of pores in the cross-sectional area. The 3D tomographic image-based X-ray CT analysis was conducted for the selected samples to show the 3D pore ratio (i.e., ratio of pore volume to total volume), the pore size, the spatial distribution of pores along the height direction of specimen, and open and closed pores. Based on the experimental results, the relationships of oven-dried density with these porosities were identified. Research findings revealed that the complementary use of these testing methods is beneficial for analyzing the characteristics of pores in cementitious materials.

2014 ◽  
Vol 8 (S1) ◽  
pp. 1-3 ◽  
Author(s):  
Kivanc Kose ◽  
Rengul Cetin-Atalay ◽  
A. Enis Cetin

Author(s):  
Vinh-Thong Ta ◽  
Olivier Lézoray ◽  
Abderrahim Elmoataz

The authors present an overview of part of their work on graph-based regularization. Introduced first in order to smooth and filter images, the authors have extended these methods to address semi-supervised clustering and segmentation of any discrete domain that can be represented by a graph of arbitrary structure. This framework unifies, within a same formulation, methods from machine learning and image processing communities. In this chapter, the authors propose to show how these graph-based approaches can lead to a useful set of tools that can be combined altogether to address various image processing problems in pathology such as cytological and histological image filtering, segmentation and classification.


2008 ◽  
Vol 3 (Suppl 1) ◽  
pp. S18 ◽  
Author(s):  
Gloria Bueno ◽  
Roberto González ◽  
Oscar Déniz ◽  
Jesús González ◽  
Marcial García-Rojo

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