A probabilistic framework for estimating the residual drift of idealized SDOF systems of non‐degrading conventional and damped structures
Bibliographic record
Abstract
Summary This paper presents a general framework for predicting the residual drift of idealized SDOF systems that can be used to represent non‐degrading structures, including those with supplemental dampers. The framework first uses post‐peak oscillation analysis to predict the maximum ratio of residual displacement to the peak transient displacement in a random sample. Then, residual displacement ratios obtained from nonlinear time‐history analyses using both farfield and near‐fault‐pulse records were examined to identify trends, which were explained using the oscillation mechanics of SDOF systems. It is shown that large errors can result in existing probability models that do not capture the influence of key parameters on the residual displacement. Building on the observations that were made, a general probability distribution for the ratio of residual displacement to the peak transient displacement that more accurately reflects the physical bounds obtained from post‐peak oscillation analysis is proposed for capturing the probabilistic residual displacement response of these systems. The proposed distribution is shown to be more accurate when compared with previously proposed distributions in the literature due to its explicit account of dynamic and damping properties, which have a significant impact on the residual displacement. This study provides a rational basis for further development of a residual drift prediction tool for the performance‐based design and analysis of more complex multi‐degree‐of‐freedom systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".