Self-healing capability of engineered cementitious composites incorporating different types of pozzolanic materials
Why this work is in the frame
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Bibliographic record
Abstract
Engineered cementitious composites (ECCs) are relatively new construction materials characterized by strain-hardening behavior under excessive tensile loading. The intrinsic selfhealing ability of cracks in ECC is an attractive methodology for durability and in some cases for regaining of mechanical properties. In order to achieve the robust self-healing criteria, this dissertation comprises of four main parts. The first part explores the effectiveness of frost action on water movement into micro-cracked ECC. Experimental findings show that until the end of 150 freezing and thawing (F/T) cycles, sorptivity measurements of ECC went down due to self-healing in micro-cracks. However, after 150 F/T cycles, deterioration surpasses the self-healing effect and increased sorptivity. The second part of the research focuses on the effect of self-healing capability on the different transport properties of micro-cracked ECC with several maturity levels and incorporating different mineral admixtures. Experimental results reveal that with the selection of proper mineral admixture, 92% recovery in water sorptivity results is attainable mostly in the first seven days of curing. The third part investigates the effects of progressively increasing sustained loading on self-healing behavior of 180-day-old micro-cracked ECC incorporating different mineral admixtures. Experimental results show that even under progressively increasing sustained mechanical loading, modulus of rupture results greater than the original values could be obtained, depending on the mineral admixture selection. The last part of the dissertation investigates the selfhealing behavior of large scale ECC reinforced beams under load influence. The results show that ECC beams with no regards to their size exhibit strength recovery after 90 days of extended moist curing. As for ductility, ECC beams with lower shear span to depth ratio have more pronounced recovery than the ones with higher ratios.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it