Research on Wind-induced Vibration Control of High-rise Steel Structures Based on Machine Learning Algorithm
Bibliographic record
Abstract
In recent years, the research on vibration control of high-rise steel structures has developed vigorously, and many research results have been applied to practical projects. The main purpose of wind-induced vibration control of high-rise steel structures is to reduce the discomfort of occupants and the damage of precision equipment and non-structural components. This kind of high-rise steel tower structure is highly flexible, and under the action of strong wind load, the dynamic response of the structure is also great, which has a very adverse impact on the safety of the structure itself, the technological requirements of the building and the comfort level, etc. Therefore, it is increasingly important to effectively control the wind-induced vibration response of the structure. Machine learning algorithm is the research hotspot of gust prediction, and its advantage lies in that this kind of method can establish the nonlinear relationship between gust related variables and gust without depending on some specific parameters. Due to the uncertainty of the control system including structural system and control, the complexity of mathematical model and difficult to grasp dynamic characteristics, the wind-induced vibration control effect of high-rise buildings needs to be further improved. In this paper, the wind-induced vibration control of high-rise steel structures is described in detail by using machine learning algorithm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".