A Computational Framework for Risk Assessment of RC Structures Using Indicators
Bibliographic record
Abstract
Abstract: The present article starts out by proposing a framework for risk assessment of RC structures utilizing condition indicators. Thereafter, the various building stones of the suggested framework are described. This description includes a summary of the basis for the probabilistic modeling of the initiation phases of chloride-induced corrosion of concrete structures. Furthermore, a probabilistic modeling of condition indicators regarding the condition state of concrete structures is proposed whereby information available at the design stage of concrete structures as well as information obtained through in-service inspections may be utilized for the purpose of reliability updating. Finally, it is described how the probability of localized and spatially distributed degradation of different degrees can be assessed and examples are given on how the various indicators may be used for the purpose of updating the statistical characteristics of the future degradation of RC structures. The presented framework forms a consistent basis for risk assessment of concrete structures subject to chloride-induced corrosion. It can easily be adopted to other degradation phenomena such as carbonation-induced corrosion and it forms a good basis for the development of efficient approaches to Asset Integrity Management of RC structures.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".