BodyText1
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 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 research, we developed 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. 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.
A paper presenting the results of this study is available as an open access publication: Chandra L. Reedy and Cara L. Reedy. 2022. High-resolution Micro-CT with 3D Image Analysis for Porosity Characterization of Historic Bricks. Heritage Science 10:83.
Funding: National Center for Preservation Technology and Training (National Park Service), grant number P19AP00143.
