Predicting the lateral capacity of short step-tapered and straight piles in cohesionless soils using an FE-AI hybrid technique
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".