Topology Optimisation of Structural Steel with Non-Penalisation SEMDOT: Optimisation, Physical Nonlinear Analysis, and Benchmarking
Bibliographic record
Abstract
In this work, Non-penalisation Smooth-Edged Material Distribution for Optimising Topology (np-SEMDOT) algorithm was developed as an alternative to well-established Topology Optimisation (TO) methods based on the solid/void approach. Its novelty lies in its smoother edges and enhanced manufacturability, but it requires validation in a real case study rather than using simplified benchmark problems. To such an end, a Sheikh-Ibrahim steel girder joint’s tension cover plate was optimised with np-SEMDOT, following a methodology designed to ensure compliance with the European design standards. The optimisation was assessed with Physical Nonlinear Finite Element Analyses (PhNLFEA), after recent findings that topologically optimised steel construction joint parts were not accurately modelled with linear analyses to ensure the required highly nonlinear ultimate behaviour. The results prove, on the one hand, that the quality of np-SEMDOT solutions strongly depends on the chosen optimisation parameters, and on the other hand, that the optimal np-SEMDOT solution can equalise the ultimate capacity and can slightly outperform the ultimate displacement of a benchmarking solution using a Solid Isotropic Material with Penalisation (SIMP)-based approach. It can be concluded that np-SEMDOT does not fall short of the prevalent methods. These findings highlight the novelty in this work by validating the use of np-SEMDOT for professional applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".