Abstract
Abstract. The paper introduces 3D least squares matching as a technique to analyze multi-temporal micro-tomography data in civil engineering material testing. Time series of tomography voxel data sets are recorded during an in-situ tension test of a strain-hardening cement-based composite probe at consecutive load steps. 3D least squares matching is a technique to track cuboids in consecutive voxel data sets minimizing the sum of the squares of voxel value differences after a 12-parameter 3D affine transformation. For a regular grid of locations in each voxel data set of the deformed states, a subvoxel-precise 3D displacement vector field is computed. Discontinuities in these displacement vector fields indicate the occurrence of cracks in the probes during the load tests. These cracks are detected and quantitatively described by the computation of principal strains of tetrahedrons in a tetrahedral mesh, that is generated between the matching points. The subvoxel-accuracy potential of the technique allows the detection of very small cracks with a width much smaller than the actual voxel size.
Highlights
In materials research, several measurement techniques are used
The 3D least squares matching (3D LSM) algorithm is applied to two volume data sets
Due to the non-linearity of the gray value distribution in the volume data, initial values have to be obtained for the 3D LSM algorithm if the movements between the epochs exceed the dimensions of the cuboid
Summary
Several measurement techniques are used. Classical instruments, that allow the observation of surfaces of specimens, are for example strain gauges, inductive displacement transducers or inclinometers. Most photogrammetric contributions in this field use image sequences of camera systems and apply digital image correlation (DIC) techniques in order to compute displacement vector fields and use them for further analysis (Hampel and Maas, 2003, Maas and Hampel, 2006, Hampel and Maas, 2009, Sutton et al, 2009, Barazzetti and Scaioni, 2010, Koschitzki et al, 2011, Liebold and Maas, 2016, Liebold and Maas, 2018, Liebold et al, 2019, Liebold and Maas, 2020, Liebold et al, 2020a, Liebold et al, 2020b). Gradient based techniques were developed (Lucas and Kanade, 1981) These techniques used an iterative least squares algorithm to compute the displacements.
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