A Bayesian belief network approach to bridge infrastructure resilience assessment against seismic hazard
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Like other infrastructure, bridges are seriously affected by natural hazards like earthquakes, floods, and hurricanes, significantly affecting communities, transportation networks, and economic development. Hence, it is essential to assess the resilience of the bridge infrastructure. This study introduces the Bayesian Belief Network (BBN) model as a strategy for assessing the seismic resilience of bridges. The BBN model is developed based on the existing literature, multiple expert opinions, and the Bayesian network approach. This method minimizes the need for a large amount of historical data. The BBN model is credible in effectively addressing complex relationships among the parameters and uncertainties associated with seismic resilience through conditional probability tables (CPTs). Enhancing the study’s analytical integrity involves conducting sensitivity, scenario, and extreme condition tests, as well as applying the model to two bridge examples. The outcome of the model analysis provides a more accurate evaluation of the bridge and improves the evaluation of bridge seismic resilience. This resilience assessment of bridge infrastructure aids policymakers, engineers, and stakeholders in constructing enduring transportation networks.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 it