Evaluation of Mechanical Parameters of Bacterial Concrete
Bibliographic record
Abstract
Conventional concrete is prone to cracking under tensile load, despite its good compressive strength.Recently, biological agents have been applied to heal the cracks in concrete, making concrete structures more serviceable.This paper mainly attempts to evaluate the mechanical properties of bacteria-based self-healing concrete.Two bacteria were selected as the bioagents in concrete, namely, Bacillus subtilis and Bacillus halodurans.The concentration of the bioagents were set to 10 5 ~10 7 cell/mL in water.Then, the two bacteria were applied to cracked concrete to cure the cracks.After curing for several days, the bacteria-based self-healing concrete was subjected to compressive and flexural tests to estimate its mechanical parameters.The results show that the self-healing concretes cured for 14d and 28d had a 7% and 18% higher compressive strength than conventional concrete, respectively; the self-healing concretes cured for 14d and 28d had a 11% and 28% higher flexural strength than the conventional concrete, respectively.Thus, the bioagents could effectively heal the surface cracks on concrete, and make the concrete imperviable.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".