Interpretation of axial Statnamic load test using an automatic signal matching technique
Bibliographic record
Abstract
The Statnamic (STN) load test is characterized by a relatively long duration and low pile velocity and acceleration compared with the dynamic load test. The estimation of the static capacity of piles and their static performance characteristics from dynamic loading tests usually requires a signal matching process. In this process, the soil parameters are varied until an acceptable match between the computed and measured responses is achieved. The soil properties obtained are then used to characterize the static behaviour of the pile. In this paper, an approach is presented to analyze the response of flexible and rigid piles during the STN load test. In this approach, a one-dimensional model is used to represent the pile-soil system accounting for nonlinear soil behaviour, slippage at the pile-soil interface, and energy dissipation through wave propagation and different types of damping. The postpeak resistance of certain types of soils is also considered in the analysis. An automatic matching technique (AMT) was developed to facilitate the signal matching process for the analysis of pile response during the STN load test. The proposed AMT has several advantages, including reducing the bias in the results due to the initial selection of the soil parameters; increasing the accuracy and reliability of computed pile capacity; and reducing computational time, thus allowing for the analysis of the test results in the field. It is then possible to compute the pile capacity and make a timely decision on its suitability. The proposed approach was used to analyze the results of six STN load tests, and the comparison between the measured and computed results was good.Key words: piles, Statnamic, pile load test, signal matching, transient load, capacity.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".