Experimental investigation of shear models for lightweight aggregate concrete deep beams
Bibliographic record
Abstract
The strut-and-tie model has become an effective design methodology for deep beams replacing the conventional beam theory that does not apply to these members due to their geometric or static stress discontinuities. This article examines the applicability of shear models for deep beams with lightweight aggregate concrete. Eight lightweight aggregate concrete deep beams were constructed and tested to failure under concentrated loading. Tests were conducted to investigate the effects of shear span-to-effective depth ratio ( a/d), ranging from 0.26 to 1.04, and an effective span–depth ratio ( l e / h), ranging from 2 to 3, on the failure mode and shear behavior of deep beams. All specimens presented a shear compression or shear-flexure failure mode. Failure from the flexure mode showed a dominant pattern with increasing a/d. The l e / h value minimally influenced the diagonal cracking and ultimate strength of deep beams. In contrast, a/d significantly affected the beam strength. Our results were compared with predictions proposed by American Concrete Institute 318-14, Canadian Standard, EC2, the Tan and Cheng model, the softened strut-and-tie model, and the simplified softened strut-and-tie model, which are all based on the strut-and-tie model. These comparisons indicated that all of these shear methods can be used to predict the shear strength of lightweight aggregate concrete deep beams.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".