Progressive Collapse Resistance of RC Beam–Slab Substructures Made with Rubberized Concrete
Bibliographic record
Abstract
Abnormal loads can produce localized damage that can eventually cause progressive collapse of the whole reinforced concrete (RC) structure. This might have devastating financial repercussions and cause numerous severe casualties. Numerical simulation, using the finite element method (FEM), of the consequences of abnormal loads on buildings is thus required to avoid the significant expenses associated with testing full-scale buildings and to save time. In this paper, FEM simulations, using ABAQUS software, were employed to investigate the progressive collapse resistance of the full-scale three-dimensional (3D) beam–slab substructures, considering two concrete mixes, namely: normal concrete (NC) and rubberized concrete (RuC) which was made by incorporating crumb rubber at 20% by volume replacement for sand. The FEM accuracy and dependability were validated using available experimental test results. Concrete and steel material non-linearity were considered in the FE modelling. The numerical study is extended to include eight new models with various specifics (a set of parameters) for further understanding of progressive collapse. Results showed that slabs contribute more than a third of the load resistance, which also significantly improves the building’s progressive collapse resistance. Moreover, the performance of the RuC specimens was excellent in the catenary stage, which develops additional resilience to significant deformation to prevent or even mitigate progressive collapse.
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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 | 0.000 |
| 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 | 0.000 |
| 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".