Response sensitivity of blast-loaded reinforced concrete structures to the number of degrees of freedomThis article is one of a selection of papers published in the Special Issue on Blast Engineering.
Bibliographic record
Abstract
Accurate analysis of reinforced concrete (RC) structures under blast loading is very complicated due to the nonlinear behaviour of concrete and reinforcement and the various failure modes to be considered. Although blast loads can excite a large number of modes due to their high frequency content, practical computational tools are usually limited to single-degree-of-freedom (SDOF) models. In addition to oversimplification, SDOF models are known to give inaccurate prediction for shear forces and support reactions. This is because accurate shear force prediction typically requires accounting for modes higher than the fundamental mode. In this study, a multi-degree-of-freedom (MDOF) model is developed that takes into account the nonlinear behaviour of RC structures and the material strength and deformation dependency on the strain rate. Using this model, a series of dynamic analyses were carried out for two typical structural members, with different combination of blast pressure and impulse. The effect of varying the number of degrees of freedom (DOF) was investigated through increasing the number of nodes used to descretize each structural member. The results of the developed MDOF model were compared to the results of available SDOF models which demonstrated the deficiencies of the latter. The developed MDOF model, with few DOF, was found to be capable of accurately predicting the dynamic shear of the modeled structural members. The model was also compared to available experimental results and showed good agreement. Changing the number of DOF also affected the pressure–impulse (P–I) diagrams for the structural member significantly, especially in the impulsive regime.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".