LRFD Calibration of Simple Soil-Structure Limit States Considering Method Bias and Design Parameter Variability
Bibliographic record
Abstract
A general closed-form solution for the reliability index (or probability of failure) of a simple linear limit-state design function with one load term and one resistance term is used to compute the resistance factor expressed in a load and resistance factor design (LRFD) format. The solution considers method bias, bias dependencies, and uncertainties in choice of nominal values of load and resistance determined as part of the project-specific design process. Uncertainty in the choice of nominal values for design is linked quantitatively to the concept of project level of understanding that has been recently adopted in Canadian design practice. All random variables are assumed to be lognormally distributed. Parametric analyses are carried out to show that ignoring possible correlations between random variables can lead to conservative (safe) values of resistance factor and in other cases to nonconservative (unsafe) values. Example LRFD calibrations are carried out using different load and resistance models for the pullout internal stability limit state of steel-reinforced soil walls together with matching bias data reported in the literature. The results demonstrate the practical influence of model type, method bias statistics including dependencies, and operational factor of safety on computed resistance factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".