Coupling EDS hypermaps and X-ray microtomography for advanced 3D microstructure characterization of cement paste: A step forward in multiscale modeling
Bibliographic record
Abstract
Investigating the microstructure of ordinary Portland cement (OPC) paste using X-ray micro-computed tomography (μ-CT) requires optimized acquisition and precise image segmentation to reliably differentiate phases. μ-CT image segmentation is challenged by the heterogeneous microstructure and limited contrast between microstructure phases in the X-ray linear attenuation coefficient. Conventional gray scale value thresholding often misclassifies phases, while previous machine learning (ML) approaches have relied on manually labeled training leading to subjectivity and replicability issues. This study proposes an innovative μ-CT image segmentation method for OPC paste, leveraging chemical information from quantitative energy dispersive spectroscopy (QEDS) mapping. The method workflow involves five steps: (1) μ-CT imaging to capture the 3D microstructure, (2) scanning electron microscopy backscattered electron (SEM-BSE) imaging and QEDS mapping to generate 2D phase maps, (3) image registration to align QEDS phase maps with μ-CT images, (4) phase separability optimization using denoising and sharpening of the μ-CT images, and (5) ML-based segmentation using the random forest approach and training labels from QEDS-derived phase maps. The proposed method effectively differentiates portlandite from hydrates matrix, as well as ferrite from clinker phases. This paper further details two main applications of the method: (1) quantification of phase assemblage, hydration degree, and particle size distribution (PSD) of residual anhydrous phases, with results compared to thermodynamic modeling, and (2) the first-ever comprehensive quantitative microstructural characterization of a grid of 32 microcube samples, revealing spatial heterogeneity in phase fractions, porosity, particle counts, and microcube volumes. These results provide critical input for calibrating and validating micromechanical upscaling models. • A novel μ-CT image segmentation method integrating QEDS mapping for cement paste 3D microstructural analysis and modeling. • Enhanced phase separation using optimized BM4D image filtering and similarity calculations. • First-ever 3D microstructural analysis of 32 cement paste microcubes. • Quantified spatial heterogeneity in phase fractions, porosity, and particle counts across microcubes. • Microstructure segmented into 5 groups: ferrite, silicates & aluminate, portlandite & calcite, hydrate matrix, and pores.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".