Enhancing Concrete Self-Healing Using Wastewater Bacteria Impregnated in Pumice Lightweight Aggregates
Bibliographic record
Abstract
Self-healing concrete presents a promising solution to counter the degradation of concrete structures caused by cracks and damage. This innovative mechanism employs the concrete's capacity to autonomously repair its cracks, effectively extending the lifespan of structures and reducing maintenance costs. Particularly in regions facing water scarcity, such as the Middle East and North African countries, the utilization of wastewater holds significant importance. This study investigates the self healing abilities of wastewater bacteria infused into lightweight pumice aggregates within concrete. Wastewater contains diverse bacteria, microorganisms, and oxygen—essential components for the self-healing process. Wastewater sourced from the North Gaza Emergency Sewage Treatment (NGEST) Plant in the Gaza Strip in Palestine, was used in this research. Several concrete samples were prepared, incorporating three concentrations of impregnated pumice lightweight aggregates (10%, 15%, and 20%) to assess their self-healing performance. The investigation monitored the compressive strength and crack closure of the concrete samples at multiple stages to evaluate the wastewater's healing potential. Results indicated a notable increase in the compressive strength among the wastewater bacteria samples compared to the reference samples after 28 days of crack induction. Moreover, the crack closure in the wastewater bacteria samples was visibly evident. The rate of crack closure showed a consistent increase at 19-, 26-, and 33-days post-crack formation. These findings are promising and align with previous research that affirmed the significant potential of wastewater in enhancing self-healing properties within concrete structures.
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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".