Predicting friction capacity of driven piles using new combinations of neural networks and metaheuristic optimization algorithms
Bibliographic record
Abstract
Friction capacity is a principal characteristic in designing driven piles. Considering the complexities in analyzing the behavior of piles, many studies have recommended the use of machine learning for this purpose. However, the used methodologies need to be updated and improved with respect to recent computational advances such as the development of optimization algorithms. In this work, three metaheuristic algorithms, namely equilibrium optimizer (EO), biogeography-based optimization (BBO), and salp swarm algorithm (SSA) are deployed to optimize an artificial neural network (ANN) for predicting pile friction capacity based on pile geometry, effective stress, and shear strength. The findings indicate the suitability of the proposed algorithms. More specifically, in the training phase, the ANN supervised by SSA yielded the most accurate results, whereas in the testing phase, the BBO-ANN outperformed the two other models. The calculated mean absolute error, Pearson correlation coefficient, and root mean square error for the models are as follows: 6.0740, 0.9385, and 7.0678 for the EO-ANN, 6.1450, 0.9440, and 6.7343 for the BBO-ANN, and 5.9684, 0.9395, and 7.1322 for the SSA-ANN. It is shown that both SSA-ANN and BBO-ANN can serve as efficient tools for the reliable design of driven piles, providing efficient computational intelligence alternatives to traditional design methods.
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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 | 0.001 |
| 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".