Reliability Verification of Unreinforced Masonry Wall
Bibliographic record
Abstract
Unreinforced masonry (URM) is known as sustainable building material and is on the top of worldwide building materials consumed in residential buildings. The reliability level of a designed URM shear walls (URMW) has major influence on safety and cost of masonry constructions. Assessing the reliability level of different URMW is the purpose of this paper.The verification methods for combination of in-plane shear and compression according to the latest version of German National Annex of Eurocode 6are presented. The design models available in the code are rephrased and direct deterministic equations are introduced to predict the capacity. Limit State and Reliability Verification of URM Wall.On this base, several limit state are established and reliability analysis using crude Monte Carlo method are run. The effect of uncertainty on assessed reliability is highlighted. The distinction between linear and non-linear application of partial safety factors are assessed. The result of reliability analysis, based on the available probabilistic information on material with uncertainty models for designed URMW is presented in the article.The principal results are the actual reliability level found in the study regarding various masonry walls designed according to the latest German National Annex code DIN EN 1996-1-1 /NA: 2012-05 on different load situation. A review on the common target reliability index for structures according to different codes is done and the assessed reliability is compared with the target value.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".