Evolutionary computing-based models for predicting seismic shear strength of RC columns
Bibliographic record
Abstract
A number of regression-based models have been proposed to quantify the seismic shear strength of reinforced concrete (RC) columns. However, most of these models suffer from a high degree of uncertainty as a result of the limited datasets used in their development and/or the classic approaches used to capture the non-linear interrelationships between the shear strength and influencing factors. To address these issues, in this work, the power of multi-gene genetic programming (MGGP), guided by mechanics, was harnessed to identify the primary influencing factors and subsequently develop efficient shear capacity prediction models for rectangular and circular RC columns. Comprehensive published datasets for the shear strength of cyclically loaded RC columns were compiled and employed to develop the MGGP-based models. The efficiency of the developed models was assessed and their performance was also compared with that of other relevant prediction models. The results showed that the developed mechanics-guided MGGP approach produced more accurate and consistent prediction models to describe the complex shear behaviour of RC columns under cyclic loading than the models available in the relevant design standards and literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".