Bio-healing for micro-crack treatment in cementitious materials: Toward a quantitative assessment of bacterial efficiency
Bibliographic record
Abstract
Bio-healing is a promising approach to enhancing natural self-healing and thus completely heal large micro-cracks (> 200 ?m) in cementitious materials. The aim of this research is to better understand bio-healing of cementitious materials in order to accelerate the healing kinetics and maximize sealing efficiency of large micro-cracks. The bio-healing approach generally consists in soaking micro-cracks in a culture medium containing a bacterial strain. However, it is difficult to precisely assess the efficiency of the bacterial-mediated precipitation in the bio-healing process with respect to the impacts of natural self-healing and precipitation induced by the culture medium. The aim of this work is to study the healing of well-defined micro-cracks on mortars subjected to more and more complex healing mechanisms. First, cracked mortars were subjected to natural self-healing, then to a precursor solution (calcium lactate), and finally, to a culture medium containing a bacterial strain. However, before this last step, an important part of this study focused on assessing the growth kinetics of a bacterial strain: Bacillus cohnii. Mortars specimens (W/C = 0.485) were submitted to controlled cracking at 28 days (under sustained load) using a mechanical expansive core. Two micro-crack categories were created (100 ± 5 ?m and 195 ± 30 ?m). The healing kinetics was evaluated from air-flow measurements that were used to compute the evolution, over time, of the apparent crack opening (1, 3 and 6 months of conservation at 23°C and 100% R.H.) Overall, self-healing was faster and more complete when cracks were soaked in calcium lactate solutions compared to natural healing. Thus, precursor solutions significantly improved the healing kinetics of the larger micro-cracks (> 150 ?m). On the other hand, the optimum growth conditions for Bacillus cohnii were evaluated at different nutrient concentrations and pH values. Finally, a method was developed in order to evaluate the bacterial activity semi-quantitatively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".