Quantifying Crack Self-Healing in Concrete with Superabsorbent Polymers under Varying Temperature and Relative Humidity
Bibliographic record
Abstract
During their service life, concrete structures are subjected to combined fluctuations of temperature and relative humidity, which can influence their durability and service life performance. Self-healing has in recent years attracted great interest to mitigate the effects of such environmental exposure on concrete structures. Several studies have explored the autogenous crack self-healing in concrete incorporating superabsorbent polymers (SAPs) and exposed to different environments. However, none of the published studies to date has investigated the self-healing in concrete incorporating SAPs under a combined change in temperature and relative humidity. In the present study, the crack width changes due to self-healing of cement mortars incorporating SAPs under a combined change of temperature and relative humidity were investigated and quantified using micro-computed tomography and three-dimensional image analysis. A varying dosage of SAPs expressed as a percentage (0.5%, 1% and 2%) of the cement mass was incorporated in the mortar mixtures. In addition, the influence of other environments such as continuous water submersion and cyclic wetting and drying was studied and quantified. The results of segmentation and quantification analysis of X-ray µCT scans showed that mortar specimens incorporating 1% SAPs and exposed to environments with a combined change in temperature and relative humidity exhibited less self-healing (around 6.58% of healing efficiency). Conversely, when specimens were subjected to cyclic wetting and drying or water submersion, the healing efficiency increased to 19.11% and 26.32%, respectively. It appears that to achieve sustained self-healing of cracks, novel engineered systems that can assure an internal supply of moisture are needed.
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) | 0.000 | 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".