Building Vibration Prediction Induced by Moving Train with Random Forest
Bibliographic record
Abstract
This paper adopts a combination of numerical simulation, field test, and Random Forest to predict the building vibration induced by moving train. First, a three-dimensional finite element model based on train-track-site soil-building system is established, and the track dynamic reaction force calculated by the train-track model is applied as an excitation to the site. On the soil-building model, this paper analyzes the influence of train speed, axle load, site soil characteristics, and distance from the building on the vibration of the building caused by the train. With the Random Forest, these different influencing factors are used as inputs, and the building vibration is the output. Thus, the prediction model of the building vibration caused by moving train is established. The prediction accuracy can be tested with the measured data. The results show that this prediction method can provide a higher prediction accuracy with the maximum error (less than 6.41%) and the average error (less than 2.29%). This method overcomes the shortcomings of traditional prediction methods and improves the accuracy of vibration prediction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".