Kernel-based Column Drift Ratios Prediction in Highway Bridges
Bibliographic record
Abstract
This study focuses on quantifying the critical parameter of column drift ratio in bridge engineering and proposes a novel kernel-based regression approach to enhance the performance-based seismic assessment of bridge systems.Traditionally, analytical methods in this field have relied on power-law functions of a single ground motion intensity measure.However, recent research has explored alternative models, though the application of machine learning (ML) approaches for bridge demand quantification and performance-based seismic assessment remains largely untapped.To address this gap, we introduce an advanced ML algorithm, specifically a kernel-based Gaussian regression approach, to estimate the column drift ratio metric for bridges.The effectiveness of the proposed model is demonstrated through its application to a representative class of highway bridges in California.The results reveal that the kernel-based model performs comparably to conventional approaches, underscoring its significance in efficiently estimating column drift ratio within the performance-based engineering framework.Importantly, the model's implications extend beyond accurate estimation, as it can inform infrastructure resilience assessments and facilitate rapid decision-making processes post-seismic events.By harnessing the capabilities of ML algorithms, this approach presents a compelling alternative to conventional methods, advancing earthquake engineering practices and providing valuable insights into the behavior of bridge systems under seismic conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".