# Fourier-Based Non-Rigid Slice-to-Volume Registration of Segmented Petrographic LM and CT Scans of Concrete Specimens

## 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.

## Key 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 slice-to-volume registration by combining 2D light microscopy (LM) of petrographic sections and 3D micro-computed tomography (μCT). We segment cracks, air pores, and aggregates in both modalities and employ feature matching alongside spatial harmonics analysis for 3D shape description. The best proposed 3D registration framework through feature matching demonstrated a success rate of 89.75%, achieving a dissimilarity of 5.21% in relative root mean square error (RRMSE) terms and thereby significantly surpassing the performance of compared 2D-only methods adapted from the body of research. Our approach enables precise, automated, and verifiable quantification of voids across CT and LM modalities and paves the way for advanced computational modeling-based methods to investigate moisture transfer mechanisms for more accurate assessments of frost damage in concrete, service life prediction models, deep learning applications for multimodal data fusion, and more comprehensive FT damage simulations.