Fuzzy Numbers Applied in Reliability Assessment of Unreinforced Masonry Shear Wall
Bibliographic record
Abstract
In order to have safe and economy construction, different sources of uncertainty should be properly characterized and considered in structural design and verification. A reliability analysis is run to assess the consistency of design process, including the uncertainty. A full probabilistic approach is an appropriate means in considering the aleatory portion of uncertainty. In dealing with epistemic uncertainty in reliability analysis, modern mathematical tools like fuzzy logic is required. The non-deterministic design in a case study on Unreinforced Masonry shear Wall (URMW) by applying fuzzy numbers has performed. Instead of uncertain deterministic data of material strength, a range of possible numbers in the form of fuzzy numbers introduced to the model, considering the experiences and the expert knowledge. The predicted capacity which is fuzzy number provide more insight into behavior of URMW. Moreover, the study on significant influence of each variable on the ultimate capacity of URMW is easier. Several reliability analysis are run using only stochastic method with using fuzzy numbers. The effect of model uncertainty on assessed reliability is highlighted. The distinction between linear and non-linear application of partial safety factors is assessed. The result illustrate the fluctuation of reliability level of URMW for a wide range of applied normal force and different materials.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".