Finite Element Based Characterization of the Creep Properties of the Cement Paste Phases by Coupling Nanoindentation Technique and SEM-EDS
Bibliographic record
Abstract
This work aims to characterize the creep behavior of the major microstructure constituents of cement paste by means of a novel approach which combines Nanoindentation Technique (NT) and Scanning Electron Microscopy with X-ray microanalysis (SEM-EDS). We first performed a large grid of nano-indentation tests on different cement paste samples. The tests were carried out in displacement-control by imposing a maximum penetration depth of 150 nm for 600 seconds. The surface was finely polished according to an optimized protocol to reach very low roughness, verified by Atomic Force Microscopy (AFM) techniques. Then, we performed the chemical analysis of all the indented points by SEM-EDS. The chemical analysis allowed selecting the indentation tests which likely correspond to pure phase. One of the advantages of such a coupling technique is that there is no need of statistically deconvoluting the distributions of the measured properties to estimate the property of the phases. For instance, the relaxation curves' load-time for the C-S-H phase was selected with and without carbonation. Finally, we perform a finite element analysis of the relaxation behavior of the selected points by means of a viscous model for concrete which splits the deviatoric long term creep and the volumetric short term creep. Eventually, this simplified model-based approach provided the creep coefficient of the so-identified Calcium-Silicate-Hydrates (C-S-H), Calcium-Hydroxide (CH) and belite (C2S), monosulfoaluminate (AFm) phases. The conclusions of this work provided new insights on the identification of the creep properties of the sub-micrometer phase composing the cement phase.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".