Reliability measures for pile foundations based on cone penetration test data
Bibliographic record
Abstract
The in situ behaviour of pile foundations is considerably influenced by variability in soil properties. Cone penetration (CPT) data are often used to determine the pile ultimate capacity. A wider range of values of the ultimate capacity are predicted when different CPT-based methods are used, as compared to using pile load test results. The present study considers inherent soil variability, measurement, and transformation variability. The undrained shear strength obtained from CPT data is considered to be a random variable. An approach to obtain load–settlement curves and the associated statistics from CPT data is suggested. Component reliability indices, based on ultimate limit state (ULS) and serviceability limit state (SLS) criteria, and system reliability indices combining ULS and SLS are evaluated. The variability in the pile–soil interface parameters and pile ultimate capacity is quantified in a Monte Carlo framework using the measured data. The effects of variability, scale of fluctuation, and limiting serviceability settlement on the reliability of pile foundations are also examined. A geotechnical database from the Konaseema site in India is utilized as an example. It is shown that the reliability based design of pile foundations considering spatial variability of soil, along with the variables associated with pile–soil interface properties, enables a rational choice of design loads.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".