Consideration of spatial variability and environmental impacts in the probabilistic design of driven piles
Bibliographic record
Abstract
This study presents a probabilistic framework for the axial design of driven pipe piles that incorporates subsurface spatial variability and quantifies the influence of soil heterogeneity on reliability and environmental performance. Traditional geotechnical designs often rely on conservative deterministic parameters that simplify natural variability, resulting in overdesign and increased environmental impact. The applied methodology combines Monte Carlo simulations, random field theory, and the Unified cone penetration testing-based design method to model pile capacity under varying degrees of spatial correlation in relative density ( D R ) profiles. Initial pile geometries were determined using conventional factor of safety (FS) criteria for a homogeneous soil profile. These were then evaluated under uncorrelated, correlated, and uniform subsurface vertical spatial variability scenarios by simulating D R as a lognormally distributed random field with varying mean, coefficient of variation, and correlation length ( θ). The resulting probability of failure ( p f ) for each design was computed and compared to the deterministic FS. The analysis revealed that p f varies by several orders of magnitude for the same FS depending on the assumed spatial structure. A streamlined life cycle assessment indicates that incorporating spatial correlation enables material reductions while maintaining acceptable p f levels, achieving up to 14% global warming potential savings compared to uniform soil profile assumptions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".