Fourier-Based Non-Rigid Slice-to-Volume Registration of Segmented Petrographic LM and CT Scans of Concrete Specimens
Mohamed Said Helmy Alabassy, Martin Christian Hampe, Doreen Erfurt, Horst-Michael Ludwig, Andrea Osburg
TL;DR
This paper introduces a new method to analyze concrete damage using advanced imaging and registration techniques for better frost damage assessment.
Contribution
A novel workflow combining deep learning and Fourier-based registration for quantifying voids in concrete using LM and CT scans.
Findings
The proposed 3D registration framework achieved an 89.75% success rate and 5.21% dissimilarity in RRMSE.
The method enables precise quantification of voids across CT and LM modalities.
It supports advanced modeling of moisture transfer and frost damage simulations in concrete.
Abstract
Cyclic freezing and thawing (FT) are a primary cause of cracking in concrete, yet current assessment procedures in Germany rely heavily on qualitative estimation using the International Union of Laboratories and Experts in Construction Materials, Systems and Structures (RILEM) capillary suction, internal damage and freeze-thaw (CIF) and Capillary de-icing freeze-thaw (CDF) tests. Although these standard tests provide a general overview of the condition of concrete damage in specimens through the estimation of water saturation through capillary suction, mass of surface delamination, qualitative open surface damage, and relative dynamic modulus of elasticity, they do not take quantitative analysis of voids, including cracks and air pores, directly into account. To address this, we propose a novel workflow utilizing deep learning-based semantic segmentation with Fourier-based…
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Taxonomy
TopicsRock Mechanics and Modeling · Concrete and Cement Materials Research · Concrete Properties and Behavior