Approximating Helical Pile Pullout Resistance Using Metaheuristic-Enabled Fuzzy Hybrids
Bibliographic record
Abstract
Piles have paramount importance for various structural systems in a wide scope of civil and geotechnical engineering works. Accurately predicting the pullout resistance of piles is critical for the long-term structural resilience of civil infrastructures. In this research, three sophisticated models are employed for precisely predicting the pullout resistance (Pul) of helical piles. Metaheuristic schemes of gray wolf optimization (GWO), differential evolution (DE), and ant colony optimization (ACO) were deployed for tuning an adaptive neuro-fuzzy inference system (ANFIS) in mapping the Pul behavior from three independent factors, namely the embedment ratio, the density class, and the ratio of the shaft base diameter to the shaft diameter. Based on the results, i.e., the Pearson’s correlation coefficient (R = 0.99986 vs. 0.99962 and 0.99981) and root mean square error (RMSE = 7.2802 vs. 12.1223 and 8.5777), the GWO-ANFIS surpassed the DE- and ACO-based ensembles in the training phase. However, smaller errors were obtained for the DE-ANFIS and ACO-ANFIS in predicting the Pul pattern. Overall, the results show that all three models are capable of predicting the Pul for helical piles in both loose and dense soils with superior accuracy. Hence, the combination of ANFIS and the mentioned metaheuristic algorithms is recommended for real-world purposes.
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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 | 0.000 | 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".