Innovative FE-AI modelling of lateral resistance of long step-tapered and straight piles in cohesionless soils
Why this work is in the frame
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Bibliographic record
Abstract
Step-tapered piles provide a cost-effective alternative to large-diameter piles for supporting transportation infrastructure subjected to significant lateral forces. However, the literature currently lacks a straightforward method for estimating the lateral bearing capacity of these specialized piles. The present study aims to equip designers with practical and reliable predictive models for the lateral capacity of long step-tapered piles in cohesionless soils. To this end, a comprehensive 3D finite element (FE) analysis was conducted to investigate the parameters influencing the performance of long step-tapered piles and to generate an extensive database suitable for evolutionary polynomial regression (EPR) modelling. The FE results indicated that the optimal design of step-tapered piles involves enlarging the cross-section over a length ( L emb ) corresponding to the depth of the apparent plastic hinge, i.e., the location of the maximum bending moment. Based on the parametric study encompassing 870 cases, two robust predictive models were developed, one for straight piles and one for step-tapered piles, using multi-objective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA). The proposed models incorporate factors related to pile geometry, bending stiffness, and the complex behaviour of soil under lateral loading. Their effectiveness was validated against field measurements for straight piles and FE results for step-tapered 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 | 0.001 |
| 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 it