Estimating static tip resistance of driven piles with bottom pile instrumentation
Bibliographic record
Abstract
A technique is presented to estimate static tip resistance of a pile during driving from embedded strain and accelerometer data located one diameter (D) from the bottom of the pile. The approach uses a nonlinear single degree of freedom system to satisfy force and energy equilibrium with a global genetic inversion approach. By balancing force and energy from inertia, damping, and stiffness against the measured tip data, the unknown parameters (mass, damping, and nonlinear stiffness) are estimated. Requiring a few seconds for analysis for each blow, the algorithm ensures a real-time assessment of static tip resistance as a function of displacement, which is important when setting pile lengths. The proposed approach was applied to four test piles at two bridge sites (Florida and Louisiana). Mobilized static tip resistances ranging from 400 to 1500 kN as a function of displacement were predicted. The predicted static resistance versus displacements compared favorably with measured values from static load tests. Interestingly, the maximum recorded increase in tip resistance in silty to clayey sands was less than 20% when piles were re-struck at times ranging from 2 to 30 days after initial drive.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".