Micro-scale uniaxial compression assessment of hygro-thermo-mechanical interactions in limestone-filler cement paste at low water-to-fine ratio
Bibliographic record
Abstract
The partial cement substitution with limestone filler (LF) enables the development of sustainable ultra-high-performance concrete (UHPC). While LF reduces the carbon footprint of cement production, its influence on the micromechanical behavior of cement paste under varying environmental conditions has not been fully understood. This study presents a twofold originality: first, by employing uniaxial compression on micrometer-sized specimens to characterize the mechanical properties of cement paste containing fine LF; and second, by assessing the effects of varying relative humidity (RH) and temperature (T) conditions, as an effective method of testing under rapidly archived hygro-thermal equilibrium. Moreover, a dual-method characterization approach, including water adsorption isotherms and X-ray computed microtomography (μ-CT) was employed to verify hygro-thermal equilibrium in the specimens and analyze the microstructure, respectively. Micro-prisms (150 × 150 × 300 μm 3 ) were fabricated using a high-precision dicing saw from cement pastes with LF additions. Micro-scale uniaxial compression tests were then conducted under controlled conditions at varying RH (10, 30, and 80%) and T (20, 40, and 60°C), considering their coupled effects. The increased RH and T levels generally decrease both compressive strength and elastic modulus showing a fairly non-linear dependence. Notably, LF not only enhances compressive strength, elastic modulus, and fracture energy, but also mitigates RH and T effects, due to the refined pore structure confirmed by μ-CT analysis. This study advances the understanding of the micromechanical properties of cement paste containing LF under varying RH and T conditions at low water-to-fine ratios, providing valuable insights for the development of sustainable UHPC.
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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) | 0.001 | 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".