An Optimization Procedure for Overhead Gantry Crane Exposed to Buckling and Yield Criteria
Bibliographic record
Abstract
The current study presents a general optimization procedure that could be used in designing of various structural applications. To validate the performance of the proposed procedure, a real life application of a custom welded I-Beam gantry crane is selected. The crane is composed of three rectangular plates with the same length and different thicknesses and widths welded together by full penetration welds over the span length to form an I-Beam profile. The thicknesses and widths of plates are to be optimized to have the minimum cross section area while respecting yield, buckling, deflection and fatigue criteria. A mathematical procedure based on Timoshenko beam theory and Crane Manufacturers Association of America (CMAA) in combination with the Genetic Algorithm (GA) is presented, and a Mathcad code is implemented to find the optimal I-Beam cross section dimensions. Nine examples are introduced for 8, 12 and 20 m crane span subjected to 10, 20 and 40-toncapacities. It is noticed that the optimized I-section configurations always show narrow and thick lower flange, wider and thinner upper flange and tall and very thin web. Theupper flange local buckling and the lateral buckling limits are achieved for all nine cases, 75% of cases for the web buckling limit, about 33% of cases for the fatigue and yield limits whereas the maximum deflection constraint is never critical. The obtained results were verified using ANSYS Workbench software with a 3D Solid Finite Element model and shown good agreement, which confirms that the proposed procedure is efficient.
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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 | 0.000 |
| 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) | 0.004 | 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".