Improving the Ductility of Concrete Beams Reinforced with Topologically Optimized Steel
Bibliographic record
Abstract
To address the sustainability challenges faced by concrete structures, various attempts have been made to optimize the reinforcement layout with a topologically optimized strut-and-tie model (STM). However, most studies have focused on theoretical discussions and the few available experimental studies have only discussed the prepeak behavior of optimized beams. The postpeak behavior, especially the ductility of beams with optimized reinforcement, has not been addressed, although it is one critical criterion for ensuring structural safety. Moreover, current topology optimization methods mostly adopt linear elastic material constitutive behavior, which neglects the intrinsic strength difference between steel and concrete material and has been found to cause low ductility in concrete beams. To address these challenges and enhance ductility with optimized reinforcement, this study proposes new frameworks for designing concrete beams with optimized reinforcement. The first framework enhances the elastic-material model-based optimized reinforcement layout with a postprocessing scheme to enhance concrete compression strut ductility. The second framework develops a new optimization formulation by introducing an asymptotic nonlinear material model, which considers both the stiffness and strength difference between concrete and steel material. An experimental and numerical program was conducted to compare the structural performance of concrete beams with optimized reinforcement from different frameworks. Results show that the new frameworks have limited impact on the peak strength but increase ductility of the optimized beams. Compared with the design from the conventional bilinear model, the design from the nonlinear model reduces steel consumption by 8.2%.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".