Strength design optimization of sandwich composite structures under heavy dynamic loads
Bibliographic record
Abstract
Abstract Recent researches have witnessed an increased interest in the Rapid Runway Repair (RRR) methods to rehabilitate damages that may be caused by different incidents, such as: natural disasters of earthquakes, floods or man-made vandalism in civil wars. RRR is a strategic process for airport operations for civil and peace-making missions. The current RRR techniques like precast concrete slabs, metal mats, and fiberglass mats have different pros and cons. The current study numerically investigates CFRP sandwich composite structure for RRR usage, where its strength is maximized by design optimization to reach the possible carrying aircraft wheel capacity and safety factors. The proposed composite structure is advantageous of no corrosion, low erection time, high capacity-to-weight ratio, same finish of runway surface and repaired area, and can be applied over spots of unlevelled or inadequate bearing capacity of 60 cm diameter. The strength of the basic design of composite sandwich structure is first assessed to its maximum allowed carrying aircraft wheel capacity by FE modelling. Secondly, The Genetic Algorithm (GA) optimization technique is applied for maximizing the strength of the composite structure webs satisfying the minimum safety factor of five failure criteria of Tsai-Wu, Tsai-hill, Hoffman, Hashin and maximum stress. Finally, the achieved results promoted the usage of the composite structure to operate at the taxiways, runways, and theoretically, landing and take-off areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".