scholarly journals Applicability of X-ray computed tomography for concrete cellular structure analysis

2021 ◽  
Vol 2124 (1) ◽  
pp. 012007
Author(s):  
M S Lebedev ◽  
M I Kozhukhova ◽  
E V Voitovich

Abstract Nowadays, the researchers of materials sciences area, use direct and indirect methods such as microscopy, porosimetry, etc. for studying structural characteristics of materials. X-ray computed tomography is among one of the modern and widely used research analytical methods that provides 3D images of solid materials without any preliminary preparation, such as crashing of sample, and violation of its structural integrity. To demonstrate the potential possibilities of X-ray computed tomography, in this research matrices with cellular structure using portland cement-based cellular concrete was studied as an example. The study showed that the pore structure of cellular concrete is dominated by capillary pores with a diameter of up to 200 um. The majority of pores did not exceed 1.6 mm in diameter, that formed during the foaming process. The calculated average size of the volumetric distribution of air voids was 0.95 mm. About 80% of large pores of a cellular concrete specimen with an average size of about 1 mm determines high porosity of the composite which is consistent with its average density values. The study of interpore structure partition using X-ray computed tomography allows for evaluation the difference in thickness from 10 um to 0.6 mm in the zones of “confluence” of large pores. The porosity of cement matrix, including individual pores with sizes from -30 to 250 um, was about 16.5%. The cement matrix is dominated by the products of cement hydration with capillary and “gel” pores. There are few nonreacted cement particles that are evenly distributed throughout the volume of the composite. To obtain more complete information about the structure of cellular concrete or any other composite, it is necessary to perform complex studies applying not only X-ray computer tomography technique, also scanning electron microscopy for evaluation of chemical analysis to identify mineral phases present and correlate them with absorption intensity of X-ray radiation on tomographic images.

Plant Methods ◽  
2022 ◽  
Vol 18 (1) ◽  
Author(s):  
Daniel Crozier ◽  
Oscar Riera-Lizarazu ◽  
William L. Rooney

Abstract Background The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality. Results Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics. Conclusions Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.


1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.


2021 ◽  
Author(s):  
Katherine A. Wolcott ◽  
Guillaume Chomicki ◽  
Yannick M. Staedler ◽  
Krystyna Wasylikowa ◽  
Mark Nesbitt ◽  
...  

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