Optimization of reinforced concrete retaining walls via hybrid firefly algorithm with upper bound strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper represents a novel hybrid optimization method that uses an improved firefly algorithm with a harmony search algorithm (IFA-HS), for optimizing the cost of reinforced concrete retaining walls. The IFA-HS is utilized to find an economical design adhering to ACI 318-05 provisions. Two design examples regarding retaining walls are optimized using the proposed hybrid method, and the optimization results confirm the validity and efficiency of the developed algorithm. The IFA-HS method offers improvements on the recently developed firefly algorithm. These improvements include utilizing the memory that contains information extracted online during a search, employing pitch adjusting operation of HS during firefly updates, and modifying the movement phase of the FA. Moreover, to decrease the computational effort of the IFA-HS, the upper bound strategy, which is a recently developed strategy for reducing the total number of structural analyses, is incorporated during the optimization process.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it