Performance Assessment of RC Columns Under Near-Field Blast Loading Using CFD Modelling
Bibliographic record
Abstract
This study investigates the effects of various design parameters on the performance of reinforced concrete columns under near-field blast loads, specifically for scaled distances less than 0.4 m/kg 1/3 .The computational fluid dynamics analysis method in LS-DYNA is used to model the detonation process of the explosive, the propagation of the blast wave, and its interaction with the structure.The model's ability to accurately predict blast loads and simulate structural response is verified against experimental data from the literature.Using the verified model, the influence of transverse reinforcement spacing, concrete cover, axial load ratio, and column cross-section shape on the structural performance is evaluated based on several parameters including the lateral displacement, the extent of the damage zone, material stress condition, and residual axial capacity.Based on the analysis results, it is concluded that a reduction in the transverse reinforcement spacing reduces the lateral displacement and spall length, while increasing the residual axial capacity.Also, a reduction in concrete cover is found to reduce spalling but has a minimal effect on the lateral displacements.Lastly, it is shown that increasing the axial load ratio significantly reduce the lateral displacement, but past a certain point can lead to shear failure near the support.
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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 | 0.000 | 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".