Development of strut and tie models for simply supported deep beams using topology optimization
Why this work is in the frame
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Bibliographic record
Abstract
Generally, structural members can be broadly divided into two regions, namely B, or Bernoulli regions, where the strain distributions are linear and D, or Disturbed regions, where the strain distributions are nonlinear While well defined theories are available for designing B regions, rules-of-thumb or empirical equations are still being used to design D regions although B and D regions are equally important. It has been recently understood that the strut and tie model is an effective tool for the design of both B and D regions. Since this method is a realistic approach, this has found place in many codes like Euro code, American code, Canadian code, Australian code, New Zealand code etc. In a deep beam, the distribution of strain across depth of the cross section will be nonlinear and hence these structural elements belong to D regions. The existing code provisions for the design of simply supported deep beams are inadequate and are empirical in nature. In this paper,the development of strut and tie models for simply supported deep beams using topology optimization is discussed. The design of deep beams using topology optimization is illustrated using an example and is compared with available code recommendations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it