A graph-based multi-objective genetic algorithm for optimizing the structural performance in double-layer shell structures
Bibliographic record
Abstract
: Discretization for fabrication and optimizing component arrangements in shell structures are crucial for enhancing structural performance and reducing material usage. Efficient optimization can significantly improve load-bearing capacity and overall stability while minimizing weight and cost. This research proposes a graph-based multi-objective genetic algorithm to optimize the arrangement and segmentation of the support layer in double-layer shell structures made of sheet material. Each solution in the algorithm’s search space represents a double-layer thin sheet metal shell with a common main shell and a unique support layer. The support layer is composed of a set of meshes reconstructed from graph segments derived from the decomposition of the dual graph of the shell’s support layer. Various subgraph arrangements are explored through crossover and mutation operations designed for graphs. The population is refined using non-dominated sorting and front ranking to identify optimal solutions. The objectives are to minimize the maximum displacement under the structure’s own weight and to reduce its overall mass. Structural analysis is used to evaluate the alternatives. The algorithm efficiently explores the solution space, producing Pareto fronts that represent optimal trade-offs between these conflicting objectives. The results demonstrate the method’s effectiveness in achieving a balanced design that meets both structural and weight constraints.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".