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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".