Assessing characteristic value selection methods for design with load and resistance factor design (LRFD) — design robustness perspective
Bibliographic record
Abstract
An important step in load and resistance factor design (LRFD) is the selection of the characteristic values of uncertain soil parameters, which can be quite subjective despite the simplicity of LRFD. This paper assesses five statistical methods for the selection of characteristic values for design with LRFD, focusing on the design robustness. A framework based on the consideration of safety, cost, and design robustness is proposed for assessing these selection methods. This framework is illustrated with an example, the design of a drilled shaft in sand using LRFD, in which the best overall method for selecting the characteristic values is suggested. The implication of the outcome of this study is quite significant in geotechnical engineering practice, as it provides guidance on the selection of the characteristic values for design with LRFD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".