Dynamic Behavior of Rocking Concrete Bridge Piers Subjected to Vehicle Collisions
Bibliographic record
Abstract
The nonlinear dynamic behavior of rocking bridge piers with different configurations was numerically investigated in this study when subjected to vehicle collisions using finite-element (FE) simulations in LS-DYNA software. The impact performances of the rocking monolithic and segmental piers were evaluated and compared to a monolithic pier considering the variations of the impact velocity (Vimp) and the axial load ratio (ALR). The FE simulations revealed that the rocking monolithic and segmental piers experienced lower peak impact forces, mitigated flexural damage, and dissipated more energy compared to the monolithic pier owing to the slippage mechanism at the joint interfaces. In addition, although an increase in the number of segments led to more severe localized concrete spalling damage, it enhanced the energy dissipation in the rocking piers. Furthermore, compared to the total collapses of the monolithic and rocking segmental piers under a high rate of vehicle impact at Vimp=140 km/h, the rocking monolithic pier demonstrated superior impact performance by withstanding total collapse owing to the higher structural integrity of the pier and lower stress concentration. Also, an absolute positive effect of the ALR on the impact resistance of the monolithic and rocking piers was obtained when the piers were subjected to medium-velocity impacts leading to relatively low deformations in the piers. However, an ALR of 0.15 was recognized as a sensitivity level of the piers’ impact resistance under a high-velocity vehicle impact with Vimp=140 km/h.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".