Experimental and Numerical Assessment of Reinforced Concrete Beams with Disturbed Depth
Bibliographic record
Abstract
Abstract This paper investigates numerically and experimentally the performance of reinforced concrete (RC) beam with unequal depths subjected to combined bending and shear. Such beams can geometrically be considered for unleveled reinforced concrete (RC) floor slab-beam system. However, it may generate critical disturbances in stress flow at the re-entrant corner (i.e. location of drop in beam depth). This research investigates the use of shear reinforcement and geometric properties to enhance cracking characteristics, yielding, ultimate load-carrying capacity, and exhibiting ductile failure mode. Ten reinforced concrete (RC) beams were constructed and tested experimentally considering the following key parameters: recess length, depth of smaller beam nib, and amount and layout of shear reinforcement at re-entrant corner. Finite element analysis (FEA) with material non-linearity was conducted in two RC beams that were tested experimentally to validate the computer modelling. The FEA models were then extended to conduct a parametric study to investigate the influence of geometric parameters (beam shape and width) and amount and arrangement of shear reinforcement on the structural response. Results confirmed that geometric properties and ratio of shear reinforcement at the re-entrant region significantly affect the behavior of reinforced concrete beam with unequal depths in terms of first cracking, yielding level, ultimate load carrying capacity and mode of failure.
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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 | 0.001 | 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 | 0.000 | 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".