A parametric study on the cost optimization of a reinforced concrete abutment using a genetic algorithm
Bibliographic record
Abstract
This paper presents the cost optimization of a reinforced concrete abutment of a cantilever type using a genetic algorithm (GA). During the optimization process, six design variables, including two geometrical design variables and four cross-sectional design variables, were considered. The objective function consists of the cost of steel, concrete, and labor. Computation programs have been developed in JAVA to find an economical design adhering to Indian Road Congress (IRC) standards. To get an optimal solution in reasonable computational time, an attempt is carried out to evaluate the optimal GA parameters for the abutment model. A parametric study was conducted to understand the effect of the angle of friction, grade of concrete, and height on the cost optimization of the abutment. From the parametric study, it is observed that optimum cost of the abutment is obtained with a higher value of the angle of friction and concrete with a lower compressive strength. The results of the optimization are further discussed in detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".