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 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) | 0.000 | 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 | 0.000 |
| 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".