Service-life prediction of concrete bridge decks using case-based reasoning
Bibliographic record
Abstract
Bridge management systems (BMSs) are developed to assist decision-makers in optimizing the allocation of their limited budget on maintenance needs of bridge networks. Reliable deterioration models are essential constituents of BMSs that are used to predict the remaining service life of bridge components. The deterioration models incorporated in the recent BMSs have limitations that can be accepted for the analysis at the network level but not at the component level. Moreover, the current mechanisticdeterioration models developed for the component level analysis are neither versatile nor adequately extensible to predict the service life of a large number of bridge components, or to incorporate additional deterioration parameters. Therefore, an artificial intelligence approach 'Case-Based Reasoning (CBR)' is proposed to develop extensible, reliable, and generic deterioration models for the analysis at the component level. The CBR approach is utilized to predict the time to corrosion initiation of the reinforcing steel inconcrete bridge decks. Data obtained from the Dickson Bridge in Montreal are used to generate the cases that populate the case library of the CBR model by applying the Monte Carlo Simulation techniques. Parameters that significantly affect the deterioration rate of concrete decks, such as the concrete cover thickness, apparent diffusion coefficient, and surface chloride concentration, are considered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".