Evaluation of Interaction between Bridge Infrastructure Resilience Factors against Seismic Hazard
Bibliographic record
Abstract
Infrastructure systems, such as bridges, are perpetually vulnerable to natural hazards such as seismic events, flooding, and landslides. This study aims to determine the relevant parameters required to increase the seismic resilience of bridge infrastructure based on the decisions of experts and prior research. To this end, the crisp DEMATEL (decision-making and trial evaluation laboratory) and rough DEMATEL methods are employed. Rough DEMATEL is a supplement to crisp DEMATEL that incorporates rough theory to handle ambiguity. The efficacies of the rough and crisp DEMATEL methods are then compared between the two approaches. This study found the most crucial seismic-resilience variables for bridges. The outcomes of this study reveal the significant order and cause-and-effect relationships. This research can assist transportation engineers and executive agencies in enhancing the seismic resilience of roadway bridges and bridge networks.
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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) | 0.000 | 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 | 0.001 |
| 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".