Improving the Prediction for the Ultimate Axial Capacity of Driven Piles in Ontario Soils with a Genetic Algorithm
Bibliographic record
Abstract
A genetic algorithm (GA) was developed to improve predictions for the ultimate axial capacity of driven piles in Ontario soils. Challenges arise to accurately predict the ultimate capacity due to many influential factors, such as the ground conditions, installation method, and pile geometry. A total of 43 piles (H or pipe piles) were collected from the Ministry of Transportation of Ontario. Side and tip resistances were extracted from piles subjected to extension and compression load tests. The soil measurements and pile resistances were regressed with a statistical analysis and GA, and the developed relationships were compared to existing design methods. On average, existing design methods overestimated the capacity by a factor of 1.16 to 3.00. The proposed correlations were slightly conservative with the capacity but provided errors within ± 30 % of the measured side resistance. The new design methods from the GA offer substantial improvements for pile 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".