Experimental investigation on repair and self-healing of tension-induced cracks in reinforced concrete panels
Bibliographic record
Abstract
This research concentrates on repair and self-healing of tension cracks in RC panels subjected to direct tensile loading and hydrostatic water pressure by incorporating high-performance materials including High-strength epoxy, Glass Fiber Reinforced Polymer (GFRP) laminate, Engineered Cementitious Composite (ECC) slag, and ECC fly ash. The test setup simulates repairing internal surface of a cracked wall segment of a containment exposed to internal hydrostatic pressure. The study showed the applicability of ECC fly ash in repairing RC specimens with the ability to restore the structural strength along with reduced cracking, leakage, and enhanced self-healing behavior. GFRP exhibited high effectiveness in waterproofing and repair of damaged RC structures. Applicability of epoxy injection in local repair of cracked RC specimens was confirmed, while brittle failure of epoxy resulted in development of a large crack and high leakage rate. Moreover, GFRP was found to be the most economical technique for repair of RC containments.
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) | 0.000 | 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) | 0.002 | 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".