The Influence of Structural Spatial Constraints on the Shear Performance of RC Beams
Bibliographic record
Abstract
This study employs a multiscale numerical approach to establish models of reinforced concrete (RC) beams under different structural spatial constraints to investigate the influence of structural spatial constraints (beam end constraints and adding cast-in-place floor slabs) on the yield mechanism of frame structures. It quantitatively analyzed the influence of end restraints and cast-in-place floor slabs on the shear capacity and stiffness of RC beams. It revealed the mechanisms by which end restraints and cast-in-place floor slabs affect the shear behavior of RC beams and compared the simulated results with the load capacity calculated results according to current codes of various countries. The study found the following: 1) Beam end restraint conditions significantly affect the beams’ failure mode, whereas cast-in-place floor slabs have little effect. 2) Changing the beam end restraint from simple to fixed supported dramatically increases the stiffness and shear capacity of the beams. Fixed-end beams can have stiffness and shear capacity up to 3.49 times and 2.66 times higher, respectively, compared to simply supported beams. 3) Adding cast-in-place floor slabs significantly increases the shear capacity and stiffness of the beams. Adding cast-in-place floor slabs to simply and fixed supported beams can increase the shear bearing capacity of the beams by 1.50 times and 1.39 times, respectively, and increase the stiffness by 2.68 times and 1.63 times, respectively; 4) The prediction of the shear bearing capacity of fixed supported beams and beams with cast-in-place floor slabs in various national codes is exceptionally conservative. The simulated values of fixed-supported plus cast-in-place slab beams with a shear–span ratio of 1.0 are 4.00 times, 4.47 times, 6.16 times, and 5.18 times higher than the calculated values in Chinese, American, Canadian, and European codes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".