Predicting the lateral capacity of short step-tapered and straight piles in cohesionless soils using an FE-AI hybrid technique
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Bibliographic record
Abstract
Offshore pile foundations are frequently subjected to significant lateral loads , often requiring large-diameter piles. Step-tapered piles have emerged as a cost-effective alternative, offering enhanced lateral capacity with reduced material use. However, reliable and straightforward methods for estimating their lateral bearing capacity remain limited. This study presents a hybrid approach combining three-dimensional finite element (FE) modeling and multi-objective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to predict the lateral capacity of short straight and step-tapered piles in cohesionless soils . A parametric study using PLAXIS 3D simulated 580 different pile cases under service-level lateral loads. The mechanisms governing the performance of step-tapered piles were examined and discussed. The FE simulation results were then used to train an artificial intelligence (AI)-based model that produces predictive equations, accurately replicating the FE outputs at a horizontal deflection of 12.5 mm while reducing computational time significantly. The study predictions were compared against the Broms' method, the Characteristic Load Method (CLM), and full-scale field test data. The developed equations account for key geometric and soil parameters, offering a practical and efficient tool for the preliminary design of laterally loaded short piles.
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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