Quantifying Seismic Resilience of Highway Bridges: A Case Study using Bayesian Neural Network
Bibliographic record
Abstract
Seismic resilience assessment of critical infrastructures is paramount for enhancing emergency mitigation planning and ensuring robust system performance in seismic hazard scenarios.This study focuses on evaluating the seismic resilience of highway bridges, crucial components of transportation networks whose proper functioning post-hazard events is essential for overall network integrity.While machine learning (ML) approaches have gained traction in earthquake engineering, their application to bridge resilience quantification remains underexplored.Improved ML algorithms, such as artificial neural networks (ANN), offer an alternative to laborintensive computational analyses while enhancing model accuracy.This study introduces a novel Bayesian approach to quantify ANNbased bridge resilience.Applied to a typical class of highway bridges in California, the proposed methodology demonstrates its efficacy in estimating seismic resilience metrics.The findings indicate that the Bayesian network performs comparably to conventional neural network approaches, underscoring its potential significance in efficiently estimating network resilience and informing infrastructure resilience-based assessments and rapid decision-making processes.This research contributes to advancing computational frameworks for resilience estimation and offers valuable insights for enhancing infrastructure resilience and emergency preparedness efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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 source (direct Gemma or distilled Codex), 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".