The Optimization of Slender Reinforced Concrete Columns
Bibliographic record
Abstract
Abstract Slenderness is an important issue in design of reinforced concrete (RC) columns. Especially for long columns, second order effects may be not so small to neglect, but the calculation of second order effects may take too much time. For that reason, ACI 318 design code includes a simple approach in order to increase the flexural moment of columns according to their slenderness. Thus, second order effects are considered. In optimization, the effect of slenderness can be considered by using the factored design flexural moments. In this paper, harmony search (HS) algorithm is employed to find the optimum design variables of slender RC columns. These design variables are web width, height, diameter and number of reinforcements. The optimization objective is total cost of materials including concrete and steel. The developed method is effective to find the optimal design for axial force, flexural moment and shear force values. As numerical examples, optimum design of columns with different lengths, but with the same loadings and material properties were investigated. Thus, the effect of slenderness was seen on the optimum costs. By the increase of column length, increase of total material cost is more than a linear increase. This situation shows us the effect of slenderness on optimum RC columns (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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 | 0.000 | 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 | 0.001 | 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".