Fuzzy-based approach for determination of characteristic values of measured geotechnical parameters
Bibliographic record
Abstract
Determination of the nominal (characteristic) values of geotechnical properties plays a crucial role within the limits states design (LSD or LRFD) concepts. The interrelationship between the process of the selection of the nominal value and the safety level is not clearly addressed in most of the new limits states design codes of practice for geotechnical engineering. Estimation of the characteristic values (p% fractile or the mean value) using the stochastic models is often linked up with some assumptions regarding the probability distribution functions. Probability theory has been perceived as a unique methodology to handle uncertainty in these geotechnical parameters despite the fact that some of the uncertainties associated with these geotechnical properties may be nonstochastic in nature. In this paper, the uncertainty connected with measured geotechnical properties is modeled using the fuzzy-reliability techniques. The measured parameters are rendered into fuzzy variables and the nominal values are characterized by fuzziness. The procedure presented is proposed as an alternative or complementary method to the estimate of the nominal values of geomaterials. The approach is illustrated with computational algorithms and a numerical example.Key words: characteristic value, nominal value, fuzzy model, fuzzy variable, resistance factor, probability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".