Smart Bio-Agents-Activated Sustainable Self-Healing Cementitious Materials: An All-Inclusive Overview on Progress, Benefits and Challenges
Bibliographic record
Abstract
Cementitious materials deteriorate progressively with the formation of cracks that occur due to diverse physical, chemical, thermal, and biological processes. Numerous strategies have been adopted to obtain cement-based self-healing materials and determine the novel self-healing mechanisms. The uses of microbes have been established to improve the thickness of the healed crack and mechanical properties of the concrete, a phenomenon seldom addressed in the literature. Based on these factors, this article comprehensively appraises the smart bio-agents-based autonomous healing performance of concrete to demonstrate the recent progress, expected benefits, and ongoing challenges. The fundamentals, design strategies, and efficacy of the smart bio-agents-activated self-healing cementitious materials are the recurring themes of this overview. Furthermore, the effects of various processing parameters on the performance of cementitious self-healing smart bio-agents are discussed in-depth. The achievements, knowledge gaps, and needs for future research in this ever-evolving area for the sustainability and resilience of the built environment are highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".