High-resolution micro-CT with 3D image analysis for porosity characterization of historic bricks

Abstract

The study of pores in historic bricks is important for characterizing and comparing brick materials, evaluating the degree of deterioration, predicting behavior in future weathering conditions, studying the effectiveness of protective measures, and analyzing the potential effects of cleaning treatments. High-resolution micro-CT coupled with 3D image analysis is a promising new approach for studying porosity and pore systems in bricks. In this technique, hundreds or even thousands of X-ray projection images are acquired at 360 degrees around a sample. The X-radiation passing through the sample is absorbed, with radiation attenuated to varying degrees depending on the varying densities of phases within the object. The 3D volume is reconstructed by a computer algorithm, producing images where each voxel has a grayscale intensity value associated with the component it represents. Recent new instrument designs allow fast scanning with good spatial resolution. In this research, we present a set of protocols for creating optimal images of brick pores in micro-CT scans and for conducting 3D image analysis to extract both qualitative and quantitative data from those scans. Small samples give better spatial resolution for imaging of pores, so given the typical heterogeneity of bricks, scanning multiple samples from each brick ensures that the results are more likely to be representative. Machine learning and deep learning with convolutional neural networks were found to be important tools for better distinguishing pores from the surrounding matrix in the segmentation process, especially at the very limits of spatial resolution. Statistical analyses revealed which of the many parameters that can be measured are potentially most significant for characterizing the pore systems of bricks. These significant pore variables came from a multi-staged image analysis approach and include the total volume percent occupied by pores, the percentage of those pores accessible to the surface versus isolated interior ones, a variety of statistical properties of individual pores related to their size and shape, the average number of connections that pores have to other pores, and the length, diameter, and directness of those connections. Graphical Abstract

Analysis of fixation materials in micro-CT: It doesn’t always have to be styrofoam

“…Styrofoam is one of the most common materials used to support or fix samples in (micro-)CT due to its specific properties [ 5 , 39 – 43 ]. This has also been shown in our measurements.…”

Detection of Porosity in Impregnated Die-Cast Aluminum Alloy Piece by Metallography and Computer Tomography

“…The relatively large sample volume allowed a resolution of 30 µm, which meant the detection of 2•× 10 8 spatial pixels (the size of the resulting file was 196 GByte). Limodin [14], Garb [15], and Weidt [16] measured approximately the same number of voxels but used a significantly smaller sample to achieve a better resolution. The results of the porosity analysis in a 3D reconstructed image are shown in Figure 2.…”

“…Recently, Zhuang et al [11] developed a method to analyse porosities below the resolution of CT. The accuracy and analysis of the results are influenced by a number of additional factors, such as transparency and segmentation [12][13][14].…”

Investigating the impact of deformation on foam permeability through CT scans and the Lattice‐Boltzmann method

“…A set of protocols for brick pores micro‐CT scans image analysis applying machine learning and deep learning is proposed in ref. [11]. Convolutional neural networks are considered as an important tool for enhancing the differentiation between pores and the surrounding matrix during the segmentation process, particularly when dealing with spatial resolutions at their utmost limits.…”