Determination of strut-and-tie models for structural concrete under dynamic loads
Bibliographic record
Abstract
This proposed study aims to develop reliable and efficient numerical optimization methods for generating optimal strut-and-tie models (STMs) in structural concrete members under dynamic loads. The numerical models are developed based on the bidirectional evolutionary structural optimization (BESO) method for the stiffness maximization problems. In this method, a controlling index based on the minimum weight and maximum stiffness is defined as the optimization criterion function and the element virtual strain energy is taken as the element removal and addition criterion. By the dynamical analysis, optimal strut-and-tie models are established based on the BESO method. Several examples are presented to show the efficiency of the proposed approach in finding optimal STMs under dynamic loads. It is shown that optimal STMs and reinforcement layouts under dynamic loads generally differ from those obtained under static loads. The developed numerical models based on dynamic responses can be used by practicing design engineers for the analysis and design of STMs in concrete structures.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".