Optimal probability‐based partial mass isolation of elevated coal scuttle in thermal power plant building
Bibliographic record
Abstract
Summary Partial mass isolation (PMI) system is a practical strategy to mitigate seismic response of the main structure. Many studies provide various formulas to estimate an optimum design solution for the structure under simplified excitation models. But the efficiency of these optimization methods under actual ground motion records requires investigation. This paper proposes a new optimization design framework, which considers the randomness of ground motions. To describe the vibration depression effects of the PMI under various records, several theoretical distributions were assumed and tested. A Weibull distribution was selected because of its best performance in the chi‐squared tests among the several theoretical distributions. A sensitivity study on the number of records was performed to ensure the accuracy of estimated parameters with a relatively small sample size. This framework was adopted in the design of a PMI system for a large‐scale thermal power plant building through both single‐objective and multiobjective optimization procedures. Optimal design results from the single‐objective optimization procedure were compared with those from traditional formulas. Additionally, with the relative displacement limitation, the Pareto optimum set was obtained from the multiobjective optimization procedure. The final design was compared with the single‐objective optimization result.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".