Calibration Of Reliability Index For Crack Width For Reinforced Concrete Members
Bibliographic record
Abstract
<div>The cracking of Reinforced Concrete (RC) members is a highly random process. However, very few studies are focused on the probabilistic studies of concrete cracking. Second Order Monte Carlo Simulation was applied to determine the reliability index of serviceability limit state for different beam design cases. A proposed equation that has been developed based on a series of experimental work and neural network analysis, to estimate the crack spacing and width in RC members. Model uncertainty was modelled randomly to account for the uncertainties in the chosen crack width model. Monte Carlo subroutine was developed to evaluate the reliability index of the performance function. The results showed that the reliability index for crack width in all generated cases were in the recommended ranges of the acceptable limits that makes the proposed equation adopted in the monitoring strategy at the serviceability limit state as a target limit for monitoring the maximum crack width. The results obtained were compared with previous research work that was performed using First Order Monte Carlo Simulation. The results obtained were similar which indicates that the adopted methodology is reliable. The target limit can be used automatically to make decision for Structural Health Monitoring (SHM) data to repair or inject cracks of RC members. A series of steps were developed to help/guide in the decision-making process, based on the crack width. </div>
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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".