Reliability-based design of deep foundations based on differential settlement criterion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Load–displacement analysis of a single deep foundation element can be accomplished by utilizing a soil–structure interaction model, such as the “t–z” model. By combining the soil–structure interaction model with a probabilistic analysis technique, such as Monte Carlo simulation, methods to rationally incorporate variability in the model parameters can be developed. As a result, the service limit state load capacity of a single deep foundation element can be computed for an allowable total head displacement. However, in design, differential settlement between individual foundation elements is often the event of interest. This paper develops a reliability-based design methodology for deep foundations based on a differential settlement design criterion. The design methodology is developed for various levels of uncertainty in the model parameters. The results are presented in the form of cumulative distribution functions that, combined with the calculated service limit state load capacity, form the basis for serviceability design of deep foundations based on a differential settlement criterion.
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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 it