Dynamic response of RC and CFFT columns under impact loading caused by vehicle collison: Numerical simulation
Bibliographic record
Abstract
In this study, the dynamic response of Concrete-filled Fiber Reinforced Polymer (FRP) tubes (CFFTS) bridge columns under vehicle collision has been numerically investigated by using a numerical model verified against experimental testing data. The former experimental study investigated the behavior of including reinforced concert (RC), unreinforced CFFT and steel-reinforced CFFT columns under lateral impact loading using a pendulum machine. A three-dimensional finite-element (FE) model was developed to simulate the impact behavior of the tested columns. The comparison between numerical results and test results showed that the numerical model captured the overall behavior of the columns with reasonable accuracy. The verified FE model was used to investigate the response of CFFT bridge columns under a heavy 45-foot tractor-trailer impact. The influence of column diameter, reinforcement detailing, and impact speed were numerically studied. It was concluded that the CFFT columns performed better than RC columns when subjected to a 45-foot tractor-trailer impact at 80 Kph. It was also concluded that the threshold failure impact velocity for the considered setup is between 90–95 kph. In addition, the CFFT column diameter and impact velocity have a great influence on the impact resistance of CFFT columns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".