Numerical Modeling of Concrete-Filled FRP Tubes’ Dynamic Behavior under Blast and Impact Loading
Bibliographic record
Abstract
A major difficulty of the analysis of and design for close-in blasts is the high variability of the blast shock waves and the complex interactions between these waves and structures. Close-in blasts also tend to be severe loads that may cause extensive damage to a structural member. If the member in question is a load bearing column, its destruction may lead to a catastrophic progressive collapse of the structure. Thus any improvement on the performance of columns under close-in blast loading is a valuable addition to knowledge. This paper outlines a numerical model built using commercially available software to predict the response of concrete filled fiber reinforced polymer (FRP) tubes (CFFTs) and regular round reinforced concrete members to impacts and close-in blasts and determine the factors influencing their response. The models were verified against drop weight impact test lab measurements and single degree of freedom blast analyses. A parametric study was conducted using the verified models to investigate the effects of diameter, reinforcement ratio, and size of the blast on the response of CFFTs. It was found that the peak displacement response was inversely proportional to all three parameters. The results of the parametric study were used to construct new pressure-impulse diagrams for experimentally tested CFFT specimens that reflect the increased capacity of such members to blast loading.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".