Nonlinear Dynamic Response of Single-Degree-of-Freedom Systems Subjected to Along-Wind Loads. II: Implications for Structural Reliability
Bibliographic record
Abstract
Part I of the two companion papers postulated and proved the capability of self-centering systems in controlling the wind-induced damage accumulations due to long-duration along-wind loads. The present Part II paper demonstrates the benefits of ductility-based wind design in terms of economics and safety through structural reliability analysis. Initially, for self-centering systems, the ductility demands are estimated for various levels of force reduction factors, structural damping, postyield stiffness ratio, natural frequency, and energy dissipation capacity. To reduce the computational cost of structural reliability analysis, empirical equations of the mean of peak ductility demands are derived in terms of the force reduction factor and natural frequency. In the reliability estimations, two limit states, the first significant yield and incipient collapse, are considered. Both analytical and simulation techniques are used to compute the failure probabilities by considering uncertainties in both the wind load effects and capacity. Overall, the results indicate that ductile self-centering systems could be designed for reduced along-wind loads and still achieve the minimum required safety level. The results also reveal that self-centering systems designed using the linear-elastic approach but additionally detailed for ductility have a significant reserve of safety against incipient collapse.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".