Evaluation of Interaction between Bridge Infrastructure Resilience Factors Against Seismic Hazard Hazard
Bibliographic record
Abstract
Infrastructure systems like bridges are constantly exposed to different natural hazards, mainly from seismic and earthquakes. Any failure on them would represent a crisis for any civilization as they represent fundamental architecture for allowing people to get transported as well as develop the logistics from materials and products. This work identifies the main factors indispensable to improve bridge infrastructure resilience and how they interact among them based on experts' judgment and previous literature. The interaction between parameters is evaluated by integrating Fuzzy theory with Decision-Making and Trial Evaluation Laboratory (DEMATEL), known as Fuzzy DEMATEL. The findings of this research demonstrate prominence order and causal-effect relations from the main resilience factors; in this way, the outcome from this study is expected to help stakeholders and decision-makers improve the resilience from bridge infrastructure against the seismic hazard.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".