Prediction of Construction-Induced Ground Vibrations Using Field Measurements and Bidirectional Gated Recurrent Unit Neural Network
Bibliographic record
Abstract
This paper proposes a sequential bidirectional gated recurrent unit (BGRU) model to predict construction-induced ground vibrations. The ground vibration time histories for twelve real construction projects in Toronto, Canada, are collected and used to develop the BGRU model. A single time-step method is used to predict the vibrations, and the time window is swept continuously over the whole training data. In addition to the BGRU method, and for comparison, two other methods, autoregressive integrated moving average (ARIMA) and random forest (RF), are used to predict the ground vibrations. The results show that the BGRU method performs much better than ARIMA and RF methods in forecasting construction-induced ground vibrations. The BGRU method captures the construction-induced and background vibrations very well, and this method remains accurate when the training data includes both background and construction vibrations. Therefore, this method can be used to predict ground vibrations in real projects where there is always a potential for missing some parts of the ground vibration data due to the malfunction of the vibration recording units.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".