Capacity of piles subject to downdrag: a comparison of North American bridge design codes and observations from a full-scale test pile program
Bibliographic record
Abstract
Negative skin friction caused by ground settlement is an important consideration for deep foundations in limit states design. However, there are inconsistencies in the methodology whereby negative skin friction and associated drag force are considered in assessing the geotechnical capacity or geotechnical ultimate limit state (ULS) in various design codes. This includes two current North American bridge design codes, the Canadian Highway Bridge Design Code, and AASHTO LRFD Bridge Design Specifications. A test pile program was developed to observe effects of ground settlement on pile settlement, capacity, and drag force. Two instrumented steel H-piles were driven through a compressible clay layer to a hard end-bearing stratum, subjected to ground settlement by constructing a 1.5 m high embankment, followed by static load testing. A load-transfer model was calibrated from the test pile program observations. Test results and the calibrated model were used to compare geotechnical ULS requirements of the two bridge design codes. It is demonstrated that drag force did not detrimentally impact pile capacity. The results showed that for conditions of the test pile program, assessing the geotechnical ULS can be more conservative when adhering to the current AASHTO LRFD Bridge Design Specifications than the Canadian Highway Bridge Design Code.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".