Interpretation of laboratory creep testing for reliability-based analysis and load and resistance factor design (LRFD) calibration
Bibliographic record
Abstract
ABSTRACT: Load and resistance factor design (LRFD) is now recommended in North American design codes for reinforced-soil structures, including internal stability limit states. The selection of load and resistance factors that appear in limit state equations is best carried out using reliability-based analysis. In this paper the conventional approach to compute the limit state for geosynthetic reinforcement tensile rupture is reviewed, and is then recast in a reliability-based analysis framework suitable for LRFD calibration using bias statistics. The paper describes how to compute bias statistics from product-specific laboratory creep tests for the reinforcement rupture limit state. A database of results from creep tests on 94 different geosynthetic products was collected from 21 different sources. A total of 1086 in-air tensile test results and 540 creep-rupture data points were examined. This database is used to compute virgin and creep-reduced strength bias statistics for three different geosynthetic product categories. The results of analysis show that variability in the prediction of creep-reduced strength is very low, and is probably captured by the magnitude of variance in the original tensile strength of the test specimens. This greatly simplifies future LRFD calibration for the geosynthetic rupture limit state. An important implication of this study for LRFD design is that creep strength reduction factors can be taken as deterministic. The paper also provides a summary of computed creep-reduction factors that is a useful reference for future estimates of this factor from laboratory creep testing, and for preliminary design purposes using allowable stress design or LRFD approaches.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".