Temperature-driven crack self-healing and performance recovery in cemented tailings materials
Bibliographic record
Abstract
Cemented paste backfill (CPB) is an innovative cementitious construction material widely used to stabilize underground mine structures and minimize the mine’s environmental impact. Understanding the factors that influence the self-healing efficiency of CPB is essential to optimize its design and improve its durability. In real-world applications, CPB structures are often exposed to varying curing temperatures, which can affect their self-healing ability. However, the specific impact of temperature on the self-healing capacity of CPB is not yet clearly established. This study experimentally investigates the effects of temperature (i.e., 2℃, 20℃, 35℃, and 50℃) on the self-healing performance of CPB. The self-healing efficiency was evaluated through observations of crack closure, mechanical strength tests, hydraulic conductivity measurements, and assessments of physical properties (i.e., porosity and void ratio). The results demonstrate that temperature significantly influences CPB’s self-healing performance. Elevated temperatures (35℃ and 50℃) enhance the self-healing process within the CPB matrix compared to room temperature (20℃), primarily due to accelerated binder hydration. The pre-cracked CPB specimens can restore their strength and achieve up to approximately 31 % higher strength than uncracked specimens after 28 days of self-healing. However, the pre-cracked specimens cured at low temperature (i.e., 2℃) exhibit low self-healing capacity, particularly at early ages. The low curing temperature significantly delays the onset of the self-healing process in CPB material. Moreover, analytical techniques reveal that an amount of healing products, mainly consisting of C-S-H, Ca(OH) 2 , and CaCO 3 , contribute to this promising self-healing performance. The findings from this paper have important practical implications for the design, mechanical stability, and durability of CPB structures, providing valuable insights for engineering practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".