Hybrid Evaluation Method of Bridge Bearing Capacity
Bibliographic record
Abstract
This study proposes a new method to assess the bearing capacity of similar bridges while avoiding the disadvantages of costly static loading tests. First, we present a detailed evaluation of the bearing capacity for a repaired pre-stressed concrete continuous-beam bridge following a ship collision. We have developed a finite element model, modified it, and combined with two other methods to evaluate its bearing capacity. The first method proposed is the bridge design code-based method, where the bearing capacity is assessed using specified design parameters. The second is the field test-based method, where the bearing capacity is evaluated using field tests combined with structural appearance observation. Considering the relative merits of these two methods, a new and improved method for bearing capacity evaluation is proposed and implemented by combining the design code, finite element model, and field loading tests. The innovation and contribution of this paper lie in obtaining modal parameters through a convenient dynamic load test to predict the static behaviour of the bridge structure based on the modified finite element model. Based on the dynamic test results, the static behaviour of the bridge, predicted by the modified finite element analysis, and the appearance test data of the bridge structure, the bearing capacity of the bridge structure is evaluated.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".