Seismic resilience assessment of urban communities using Bayesian network
Bibliographic record
Abstract
The importance of the disaster resilience of urban communities becomes substantial as people and capital are integrated and affect each other both directly and indirectly. This study presents three main contributions to quantify the seismic resilience performance of an urban community and support the decision-making process. First, a Bayesian network (BN)-based regional seismic resilience assessment framework is developed to estimate the seismic losses of an urban community considering the structural deterioration efficiently and effectively. Second, a resilience assessment framework is developed, which estimates the reliability and recoverability indices of every district in the urban community. A concept of resilience limit-state is introduced with a graphical representation tool. Third, a retrofit strategy is proposed from the disaster resilience perspective, which facilitates finding the optimal scheme considering the multidimensional aspects of the urban community. An urban community consisting of 10 districts mimicking the downtown Vancouver area is introduced as a numerical example to demonstrate the efficiency and applicability of the findings.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 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 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".