Cracks in concrete
Journal:
arXiv
Published Date:
Jan 30, 2025
Abstract
Finding and properly segmenting cracks in images of concrete is a challenging
task. Cracks are thin and rough and being air filled do yield a very weak
contrast in 3D images obtained by computed tomography. Enhancing and segmenting
dark lower-dimensional structures is already demanding. The heterogeneous
concrete matrix and the size of the images further increase the complexity. ML
methods have proven to solve difficult segmentation problems when trained on
enough and well annotated data. However, so far, there is not much 3D image
data of cracks available at all, let alone annotated. Interactive annotation is
error-prone as humans can easily tell cats from dogs or roads without from
roads with cars but have a hard time deciding whether a thin and dark structure
seen in a 2D slice continues in the next one. Training networks by synthetic,
simulated images is an elegant way out, bears however its own challenges. In
this contribution, we describe how to generate semi-synthetic image data to
train CNN like the well known 3D U-Net or random forests for segmenting cracks
in 3D images of concrete. The thickness of real cracks varies widely, both,
within one crack as well as from crack to crack in the same sample. The
segmentation method should therefore be invariant with respect to scale
changes. We introduce the so-called RieszNet, designed for exactly this
purpose. Finally, we discuss how to generalize the ML crack segmentation
methods to other concrete types.
