SYSTEM RELIABILITY ASSESSMENT OF 3D STEEL FRAMES DESIGNED PER AISC LRFD SPECIFICATIONS
Bibliographic record
Abstract
This paper presents the system reliability analysis of a three-dimensional steel frame designed per AISC LRFD with respect to the collapse limit state under the dead and live loads.The system reliability is evaluated using the first-order reliability method (FORM)-based adaptive response surface approach.The uncertainties in the material properties, geometric properties of frame members, initial geometric imperfection of the structure, dead load and live load as well as the spatial variability of the live load are accounted for in the reliability analysis.The analysis results suggest that the system reliability of the example frame is similar to that of the planar steel frames designed per AISC LRFD and evaluated in a previous study.It is observed that the spatial variability of the live load leads to a decreased system reliability.Results of the sensitivity analysis indicate that the failure probability of the example frame increases by almost one order of magnitude if the coefficient of variation of the steel yield strength increases from 0.06 to 0.1.Furthermore, the system reliability decreases drastically if the upper bound of the magnitude of initial geometric imperfection is greater than 0.4% of the overall height of the example frame.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".