Optimum design of composite conical tanks under hydrostatic pressure
Bibliographic record
Abstract
Elevated tanks are used all over the world to store water for times of shortage. These tanks can be made of steel, reinforced concrete, or composite, that is, concrete and steel. Composite tanks consist of an external steel shell attached to an internal reinforced concrete wall through steel studs. Composite conical tanks combine the advantages of reinforced concrete and steel tanks as they resist efficiently both tensile and compressive stresses. A comparison showed that the material cost of composite conical tanks is significantly less than that of steel or reinforced concrete tanks having the same layout dimensions. A numerical tool is developed to obtain the optimum design of composite conical tanks under hydrostatic pressure incorporating both finite element and genetic algorithm techniques. This tool is used to obtain the optimum design of a case study composite conical tank that was recently constructed. The developed optimization tool provides the thicknesses of the concrete and steel walls as well as the stud configuration corresponding to the minimum material cost. A comparison between the optimized and unoptimized case study composite tank revealed that a reduction of 32% in the material cost can be achieved. A sensitivity analysis is conducted by changing the price of concrete, steel plate, and studs by (±) 50% of the datum prices and obtaining the corresponding optimum design variables. This analysis showed that the optimum thicknesses of the concrete wall and steel shell as well as studs’ configuration are significantly sensitive to the change in the material prices.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".