Performance Evaluation of CFDST Bridge Columns Under Blast Loading
Bibliographic record
Abstract
In this study, the behavior of concrete filled double skin steel tubular (CFDST) composite columns with various cross-sections and reinforcements under explosive loads was investigated.Five column models, each 5 meters in length with an inner diameter of 50 cm and an outer diameter of 80 cm, were simulated using ABAQUS/Explicit software.Column 1 had no reinforcement, Column 2 included four linear reinforcements, Column 3 had four square reinforcements, Column 4 combined linear and square reinforcements, and Column 5 featured trapezoidal reinforcements.Explosive loading equivalent to 250 kg of TNT was applied at different distances, with an optimal distance of 4.5 meters identified for all models.Additionally, a 30×10-meter bridge model, resembling a bridge in Basra, was simulated using the weakest (Column 1) and strongest (Column 5) columns.Explosions were analyzed for scenarios above the bridge and below the columns.The results demonstrated that Column 5 significantly outperformed Column 1 in resisting stresses, tensile and compressive damage, and displacements.In the scenario with explosions beneath the bridge, the performance of the bridge with Column 5 improved by up to 40%.Furthermore, tensile damage in the concrete was found to be considerably greater than compressive damage, underscoring the necessity of reinforcing concrete against tensile forces.The CFDST columns for bridge is found be the most effective among in reducing the dynamic effect induced by the blast loading on the structure.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".