Generation of eco‐friendly design for post‐tensioned axially symmetric reinforced concrete cylindrical walls by minimizing of CO<sub>2</sub> emission
Bibliographic record
Abstract
Summary Structures should be designed in the direction of providing different vital requirements such as safety, durability, strength besides comfortable, and serviceability needs to be intended for usage purposes. Also, an effective structural design must carry on the other significant conditions consisting of being economic, even not destructive to the environment via various hazardous effects. Within the scope of this study, to realize all mentioned aims, an optimization process was carried out to generate an eco‐friendly and cost‐effective structural model for a post‐tensioned axial symmetric reinforced concrete cylindrical wall. While this process is realized, three different metaheuristic algorithms as harmony search (HS), teaching–learning based optimization (TLBO), and flower pollination algorithm (FPA) were benefited to observe optimal parameters and main objective conditions of different variations produced intended for the wall structure. These optimal conditions contain optimal section size as the thickness of the wall, value of post‐tensioning loads, and their coordinates applied along the wall, besides the main purpose is to minimize of emission amount of carbon dioxide (CO2) from the utilized structural materials namely, concrete, steel reinforcements, and post‐tensioning cables. As doing this, optimal levels for arising costs of materials can also be observed at the same time. With this study, all of these processes were provided with respect to many design combinations by utilizing various strength alternatives for concrete and even steel reinforcement grades together with different structural properties such as wall height, specific weight of liquid within the wall, and number of post‐tensioning loads. By this means, it was made possible to generate both nature‐friendly, cost‐effective together with reliable and sustainable structures. The investigation of the optimum design was done for three cases. The first case was done for limited variation of design constants, and the best effective algorithm was found as FPA after the evaluation of results for multiple cycles of the optimization process. The other cases were done for different values of design constants by using the best algorithm. For the evaluation of the optimum cost for different countries, the most expensive ones are for Germany and Canada. As the final finding, the increase in the number of post‐tensioning loads reduces the CO2 emission in the optimum design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".