Barge Bow Force–Deformation Relationships for Bridge Impact-Resistant Design: Development and Assessment Using Shock Spectrum Approximation
Bibliographic record
Abstract
Force–deformation relationships of waterway vessels play an important role in the impact-resistant design of bridge structures. Characterizations of barge bow force–deformation (i.e., crushing) behaviors found in design provisions and previous research are reviewed as part of the present study. Results obtained from use of the relationships in impact analyses are then compared with computed responses from high-resolution finite-element barge–bridge collision simulations. As motivated by the comparisons, new relationships are proposed to further enhance designer capabilities for head-on barge impact design. In developing the proposed relationships, a parametric study of nonlinear dynamic collision simulations is performed to account for impacted pier surface geometry and barge bow versus impacted surface widths. Considerations are also made for impact velocities and peaks in force magnitudes that can occur for deformations near to the onset of nonlinear bow crushing. Merits of the proposed force–deformation relationships are then assessed via the shock spectrum approximation method. Key characteristics of barge bow force–deformation relationships (e.g., initial stiffness, maximum force, residual force plateau, impulse) are identified across typical ranges of bridge vibration periods and also in relation to propensities of empirical curve components for bringing about severities in computed structural demands.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".