Intelligent computing for modeling axial capacity of pile foundations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the CPT results, which have been found to yield more reliable soil properties; hence, more accurate axial pile capacity predictions. In this paper, one of the most commonly used artificial intelligence techniques, i.e., artificial neural networks (ANNs), is utilized in an attempt to develop artificial neural network (ANN) models that provide more accurate axial capacity predictions for driven piles and drilled shafts. The ANN models are developed using data collected from the literature and comprise 80 driven pile and 94 drilled-shaft load tests, as well as CPT results. The predictions from the ANN models are compared with those obtained from the most commonly used available CPT-based methods, and statistical analyses are carried out to rank and evaluate the performance of the ANN models and CPT methods. To facilitate the use of the developed ANN models, they are translated into simple design equations suitable for hand calculations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it