Hybrid simulation of rockfalls impact on bridge structure based on OpenSees-MATLAB-VecTor2
Bibliographic record
Abstract
Compared with the traditional experiment, the finite element (FE) simulation method is more cost-effective and more efficient. Hence, it is widely used to analyze the impact response of the structure. However, single structural analysis software is often only useful for some specific problems, and no one can be applied to solve all types of structural problems. A software that is widely used to analyze the impact response of structures, i.e., LS-DYNA, has been proved to be flawed in capturing the shear behavior of certain structures. Taking into account the shortcomings of the existing analysis software, this study has developed a hybrid simulation method based on OpenSees-MATLAB-VecTor2 to analyze the dynamic behavior of the bridge structure under rockfall impacts. The modeling details of the hybrid model are introduced in detail in this study. The single-machine model is built to compare the damage of the bridge column under the impact of rockfalls with the hybrid model. The results show that the impact point displacement of the impacted column simulated by the hybrid model is greater than that of the single-machine model because the hybrid model can capture the shear damage behavior of the bridge column.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".