Optimisation of Shear and Lateral–Torsional Buckling of Steel Plate Girders Using Meta-Heuristic Algorithms
Bibliographic record
Abstract
The shear buckling of web plates and lateral–torsional buckling are among the major failure modes of plate girders. The importance of the lateral–torsional buckling capacity of plate girders was further evidenced when several plate girders of a bridge in Edmonton, Alberta, Canada failed in 2015, because insufficient bracing led to the lateral buckling of the plate girders. In this study, we focus on the optimisation of the cross-sections of plate girders using a well-known and extremely efficient meta-heuristic optimisation algorithm called the harmony search algorithm. The objective of this optimisation is to design the cross-sections of the plate girders with the minimum area that satisfies requirements, such as the lateral–torsional buckling load and ultimate shear stress. The base geometry, material properties, applied load and boundary conditions were taken from an experimental study and optimised. It was revealed that the same amount of load-carrying capacity demonstrated by this model can be achieved with a cross-sectional area 16% smaller than that of the original specimen. Furthermore, the slenderness of the web plate was found to have a decisive effect on the cost-efficiency of the plate girder design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".