A Novel Performance-Based Methodology for Seismic Evaluation of Bridges
Bibliographic record
Abstract
Seismic hazards pose critical challenges to bridge safety, amplified by inadequate consideration of nonlinear responses and soil-structure interaction (SSI).Existing assessment methodologies often fail to address these complexities, limiting their applicability in ensuring structural resilience.This paper introduces a novel performance-based seismic evaluation (PBSE) framework tailored for bridges, incorporating advanced modeling and probabilistic techniques to quantify collapse risks.The framework emphasizes nonlinear dynamics, detailed SSI effects, and ground motion variability to improve predictive accuracy.The proposed methodology is applied to the Meloland Road Overcrossing (MRO) in Southern California as a case study.Four archetype models with varying degrees of SSI representation were developed and analyzed using incremental dynamic analysis (IDA) with 22 ground motions.These ranged from simplified SSI assumptions to a detailed model (D4) that explicitly captures abutment-soil and pile-soil interactions.Findings demonstrate that SSI representation critically impacts collapse predictions.The detailed D4 model exhibited increased collapse probabilities and reduced collapse margin ratios compared to simplified models, highlighting the essential role of accurate SSI characterization.Fragility curves illustrated the interplay between ground motion characteristics and SSI effects, revealing their influence on failure sequences and overall structural performance.This research redefines seismic evaluation by addressing key uncertainties in collapse risk assessments and advancing SSI modeling techniques.The PBSE framework provides engineers with a robust tool for designing resilient bridge structures and optimizing retrofitting strategies.The proposed methodology contributes to the development of safer infrastructure capable of withstanding future seismic events by accounting for ground motion characteristics and addressing various aspects of data and modeling uncertainties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".