Automated Finite-Element-Based Validation of Structures Designed by the Strut-and-Tie Method
Bibliographic record
Abstract
Several codes of practice now support the use of the strut-and-tie method (STM) for the design of complex regions in structural concrete. In this method, a load-resisting truss is idealized and designed to carry the applied forces through these regions to their supports. The method assumes that the load can be carried in the manner envisioned by the designer and that the nominal design strength is at least equal to the calculated capacity of the idealized plastic truss. These assumptions are not always valid, particularly for nonductile and complex structures, as revealed by experiments in which some of STM designed structures have exhibited poor performance at service load levels and/or not been able to support their calculated nominal design strength. Thus, there is clearly a need for a convenient and reliable means of assessing the likely performance of complex regions designed using the STM. This paper presents an integrated STM design and computational framework that was developed to overcome the barriers to efficient design by the STM and effective design validation by nonlinear finite-element analysis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".