Reinforcement layout design for deep beams based on bi-objective topology optimization
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Bibliographic record
Abstract
Determining an effective and efficient reinforcement layout for reinforced concrete deep beams is still a challenging problem. This paper proposes a bi-objective evolutionary structural topology optimization method for the reinforcement layout design . Based on a discrete model for steel bars, the optimization aims to achieve a uniform stress distribution of steel rebars while promoting the peak rebar stress. A lexicographic method is used to solve the bi-objective optimization. Two numerical examples are presented to demonstrate the proposed algorithm's feasibility, stability and universal applicability. It is shown the reinforcement stresses in both tension and compression zones are more uniformly distributed and, on average, closer to the yield strength than those of the single-objective optimization. Compared with the design based on the conventional strut-and-tie method, the deep beams optimized by the proposed method use less reinforcement steel, provide a higher ultimate load capacity, and, more interestingly, fail in a ductile failure mode.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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