Performance characterisation of machine learning models for geotechnical axial pile load capacity estimation: an enhanced GPR-based approach
Bibliographic record
Abstract
Traditional methods for predicting axial pile capacity often rely on simplified assumptions, leading to potential inaccuracies in diverse geotechnical conditions. This study investigates the key parameters influencing axial pile capacity through comprehensive statistical and machine learning analyses in Kano, Nigeria. Six models–Bayesian additive regression trees (BART), explainable boosting machine (EBM), gradient boosting machine (GBM), Gaussian process regression (GPR), improved GPR, and multivariate adaptive regression splines (MARS)–were evaluated using a dataset of 100 training and 25 testing samples from reinforced concrete piles. Statistical analysis revealed strong correlations between pile length and embedment depth (r=0.94) and moderate correlations between axial capacity and length (r=0.78). The improved GPR model demonstrated superior performance with the highest R2 (0.9873) and lowest MARE (0.0678) during training, maintaining robust performance during testing (R2=0.9812). Feature importance analysis identified the uncorrected number of blows at the pile tip (N) as the most influential parameter (35.2–42.3%), followed by embedment depth (17.97–32.8%). Partial dependence analysis revealed non-linear relationships between design parameters and axial capacity, with diminishing returns observed beyond certain thresholds. These findings provide valuable insights for optimising pile foundation design in geotechnical engineering applications.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| 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".