Experimental and theoretical studies of laterally loaded finned piles in sand
Bibliographic record
Abstract
Large lateral loads may act on pile foundation supporting structures, such as bridge abutments, retaining walls, and structures subjected to wind–earthquake loads. A pile with fins is a newly developed type of pile foundation that is capable of supporting large lateral loads. In the present study an attempt is made to evaluate the improvement in lateral capacity of a pile with fins mounted close to the pile head. Small-scale model tests and a numerical study using finite element analysis were performed on regular piles without (fins) and piles with fins. These piles were installed in sand of different relative densities (Dr= 35% and 78%). The investigations were carried out by varying the length, width, and shape of the fins, and type of pile. Results reveal that there is a significant increase in lateral resistance of the piles after mounting the fins close to the pile head. The increase in lateral resistance gained by placing fins on a pile varies with geometries of the pile and fins. The lateral resistance increases with the increase in length of the fins until the fin’s length is equal to 0.4 of the pile length. Based on the results of the laboratory model and numerical analysis, critical values of fin parameters for maximum improvement are suggested. The agreement between observed and computed results is found to be reasonably good in terms of ultimate lateral load and fin efficiency. A comparison between the model results and the prototype-scale results is also studied.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".