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

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.