Use of extended finite element method and statistical analysis for modelling the corrosion-induced cracking in reinforced concrete containing metakaolin
Bibliographic record
Abstract
This study presents an improved technique to predict the time for corrosion-induced cracking in concrete containing metakaolin (MK) based on combining extended finite element model (XFEM) and statistical analysis. The prediction model was developed based on the percentage of MK in the mixture, binder content, water-to-binder (W/B) ratio, and concrete cover thickness. The developed model was also validated experimentally using an accelerated corrosion test. Moreover, design charts were developed in this study using statistical analysis to facilitate and simplify the use of the prediction model. The results indicated that the corrosion pressure required to crack the concrete cover increased with higher percentages of MK, higher binder content, and (or) lower W/B ratio. The most significant factors affecting the time for corrosion-induced cracking was found to be the concrete cover, W/B ratio, MK replacement, and binder content, respectively, in order of significance. The results also indicated that the time required for corrosion-induced cracking obtained from the developed prediction model showed a good agreement with the experimental results of the accelerated corrosion samples. Also, the cracks predicted by the XFEM showed a similar trend of variation with that found in the accelerated corrosion samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".