Calibration of Information-Sensitive Partial Factors for Assessing Earth Slopes
Bibliographic record
Abstract
Use of the limit state design with the calibrated load and resistance factors (or partial factors of safety) has a long tradition, especially for structural design codes. The load and resistance factors are calibrated using statistics, reliability, probabilistic analyses and selected target safety levels. To take advantage of the reliability-based design approach, to achieve a greater consistency in the safety level for designed or assessed earth slopes, and to cope with the degree of uncertainty in soil properties, in this study, calibration of the information-sensitive partial factors is carried out. The calibration is based on the first-order reliability method, and considers that the critical slip surface for a given set of soil properties and geometric variables of slope can be estimated based on the generalized method of slices. The calibrated factors depend on the degree of uncertainty in the soil properties (i.e., coefficients of variation of cohesion and friction angle), and on the selected target reliability levels. Results of calibration are used to develop empirical equations for estimating the partial factors that are to be used for slope stability analysis and to assess the adequacy of slope for a selected target safety level. It is hoped that the developed relations could be used to aid the development of reliability-consistent design and checking of earth slopes, and to promote the practical application of the limit state design in geotechnical engineering.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".