Geotechnical resistance factors for ultimate limit state design of deep foundations in frictional soils
Bibliographic record
Abstract
This paper investigates the probabilistic nature of ultimate limit state failures of deep foundations in purely frictional soils (e.g., sands). In so doing, the theory required to predict both the probability of ultimate limit state failure and the resistance factors needed to avoid this limit state are proposed. The proposed resistance factors are functions of site understanding and failure consequence, and the theory leading to these resistance factors is validated via Monte Carlo simulation of a two-dimensional spatially variable random field. In both the theory and the simulation, a pile is assumed to be placed vertically at a certain position in the soil mass, and the soil is sampled at various distances from the pile to come up with characteristic soil properties (namely friction angle) for use in the pile design. Agreement between theory and simulation is found to be very good. The theoretical model is then employed to determine upper bound geotechnical resistance factors, which can be used to complement current ultimate limit state design code calibration efforts. An example of such a calibration is presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".