Influence of Snow Load Distribution on the Stability of Single-layer Reticulated Shells
Bibliographic record
Abstract
The snow load distribution has a significant impact on the stability of single-layer reticulated shells, a conventional type of long-span space structure.This paper attempts to disclose how the snow load distribution influences the stability of two types of single-layer reticulated shells, namely, spherical reticulated shell and cylindrical reticulated shell.Two influencing factors were taken into account: the asymmetry of snow area on the projection surface of the shell, and the non-uniformity of snow thickness along the radial direction of the shell.The nonlinear finite-element program ANSYS was adopted to calculate the bearing capacities of the two shells under different snow load distributions, in the light of the equilibrium path under each distribution, and to identify the most dangerous distribution of snow load for each shell.The results show that: the asymmetry and non-uniformity have obvious impacts on the stability of spherical reticulated shell, and even greater impacts on that of cylindrical reticulated shell; the most dangerous snow load distributions for spherical reticulated shell and cylindrical reticulated shell are the non-uniform distribution across the half span of the two outermost rings, and the non-uniform distribution across the half span in the middle, respectively.The research results provide reference for keeping large public buildings safe under snow load.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".