Probabilistic analysis of drilled shaft service limit state using the "<i>t</i>–<i>z</i>" method
Bibliographic record
Abstract
The utility of the load and resistance factor design (LRFD) approach is being increasingly recognized for the design of drilled shafts. The current LRFD methodologies of drilled shaft design would be more efficient if reliability based design approaches were used for service limit state design. In this paper, the "t–z" methodology is utilized to develop probabilistic approaches for axial service limit state analysis of drilled shafts. Two different models of the soil–shaft interaction are implemented for load displacement calculations: (1) an ideal elastoplastic model, and (2) a hyperbolic model. For both of these soil–shaft interactions, Monte Carlo simulation is used to obtain a large set of load–displacement curves assuming lognormal distributions for the shaft–soil interface properties. The load–displacement curves are analyzed to develop two alternate methods for determining the probability of drilled shaft failure at the service limit state. As a result, cumulative distribution histograms are developed for drilled shaft load capacities at allowable head displacements. These results may be utilized to obtain resistance factors that can be applied to LRFD based service limit state design.Key words: drilled shaft, serviceability, failure probability, load displacement relation, "t–z" method.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".