Numerical study of precast segment columns with shear keys under vehicle collision
Bibliographic record
Abstract
It is commonly understood that precast technology has been rapid development and application due to its short construction period and environmental friendly, etc. Among all precast structure, precast segment column has been widely concerned in the substructure of bridges. The precast segment columns will not only suffer from the earthquake loads but also be subjected to the collision of vehicle and barges. The concrete segments of precast segment column subjected to vehicle collision are prone to slip and even punching failure. In order to enhance the shear resistance between concrete segments, the precast segment column with shear key is designed, and the dynamic performance and shear mechanism under vehicle collision are analysed in this work. The results indicate that the shear key improves the shear capacity between segments and effectively reduces the relative displacement between segments, and makes the deformation mode of precast segment column change from shear type to bending type under vehicle collision. The maximum impact force of precast segment column with shear key is larger than that without shear key. Besides, the shear mechanism of precast segment column is revealed, and the calculation formula of local shear resistance is proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".