Restoration curves for infrastructure: preliminary case study on a bridge in Quebec, Canada
Bibliographic record
Abstract
As critical infrastructures, bridges play an important role in the resilience and functionality of the transportation system. Typically, in the aftermath of an earthquake, the restoration process of the impacted region begins. It is vital for bridges to maintain their functionality and serviceability during this period in order to expedite the restoration process. In this regard, restoration functions are used to assess the functionality of bridges in a quantitative manner before an extreme event. This paper presents probabilistic resilience curve for the Chemin des Dalles Bridge (CDB) in Quebec, Canada by incorporating fragility and restoration profiles available in the literature. The CDB is designed according to older design codes, which they may lack seismic detailing, and it could therefore be susceptible to future earthquake damage. The resilience curves obtained were used to quantify the resilience of the CDB. The results indicate that the bridge has a resilient performance in a code-level earthquake. However, in order to improve resiliency in stronger events, the retrofit of vulnerable components should be considered.
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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 | 0.001 |
| 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".