Quantification of self-healing in bacteria-based engineered cementitious composites
Bibliographic record
Abstract
Crack repair in concrete is crucial, since cracks are the main cause of decreased service life in concrete structures. An original and promising way to repair cracks is to pre-incorporate healing agents inside the concrete matrix to heal cracks the moment they appear. By incorporating bacteria and nutrients as a two-component healing agent, the process of bacterially mediated calcium carbonate deposition is triggered on crack formation, and self-healing of cracks can be expected. This paper investigates the recovery of mechanical properties due to self-healing behaviour of bacteria-based engineered cementitious composites (ECCs). In this research study, Sporosarcina pasteurii and Bacillus subtilis subsp. spizizenii were selected as two different bacterial strains, with zeolite as a protective carrier. Four-point bending and ultrasonic pulse velocity tests were performed on ECC specimens to evaluate their mechanical properties during damage and healing processes. Microstructural observations to quantify self-healing compounds were performed using X-ray diffraction analyses and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy. The bacteria-incorporated ECC specimens were found to be promising in the healing of cracks (showing total healing of 80 μm wide cracks) and recovery of flexural strength and stiffness.
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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) | 0.000 | 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 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".