Topological optimization of frame structures with stiffness and strength constraints
Bibliographic record
Abstract
Based on the ICM(Independent Continuous Mapping) method,different filter functions for element weight,element allowable stress and element stiffness are introduced to change the 0~1 type discrete topological variables to continuous topological variables between 0 and 1,so a topological optimization model with continuous topological variables is built.The stress constraints are transformed into movable lower limits of topological variables with the full stress criterion and the displacement constraints are transformed into explicit expressions with the unit virtual load method,thus the topological optimization model is explicit.To improve the solving efficiency,the dual model of the original optimization model is solved according to the dual theory by iteratively solving the dual model in its dual space.Three criteria which are no singular structure,no violated constraints of structural responses and no changed structural weight are introduced to judge iteration convergence.According to the three criteria,an appropriate doorsill is found by self-adaptively adjusting a discount factor,and then the continuous topological variables can be regressed to the 0~1 type discrete topological variables.With the opening of MSC/Nastran and the PCL(Patran Command Language) environment of MSC/Patran,the topological optimization program of frame structures with multiple variables is implemented,which can satisfy the stiffness and strength constraints.Numerical results show that it is speedy and efficient to solve the topological optimization problem of frame structures with ICM method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".