Evaluating the benefits of lightweight cellular concrete as embankment fill for reducing negative skin friction on abutment piles
Why this work is in the frame
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Bibliographic record
Abstract
Lightweight cellular concrete (LCC) offers advantages in geotechnical applications by reducing surcharge pressures compared to traditional fill materials. This study examines the use of LCC as embankment fill and evaluates its effects on negative skin friction in pile foundations, in comparison to traditional granular backfill, through field measurements and numerical simulations on a production H-pile at a bridge construction site. A verified numerical model was utilized to calculate the maximum axial force at the neutral plane, drag force, and downdrag magnitudes. The model was then modified using LCC parameters to assess potential benefits for piled foundations. Parametric analysis evaluated how LCC property variations influence axial forces in pile groups for the two representatives most and least critical positions based on the axial force magnitude. Results demonstrate that LCC reduces maximum axial force at the neutral plane by over 60%. Negative skin friction and drag force decreased by 37.5% and 65%, respectively, at the critical pile position. During filling stages with LCC, compressive forces along edge piles were reduced, though this trend reversed during consolidation. Variations in Poisson’s ratio and elastic modulus had a more pronounced influence on the pile located at the edge of the cap, while changes in unit weight impacted middle piles more substantially. LCC implementation reduced embankment settlement and downdrag by decreasing the relative settlement between soil and pile by up to 70% at the most critical location.
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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 | 0.000 |
| 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