Development of Ground Movements Due to a Shield Tunnelling Prediction Model Using Random Forests
Bibliographic record
Abstract
In order to predict the exact amount of maximum surface settlement value, this paper presents a method to predict ground movement above tunnels with random forests (RF). Surface settlement above a tunnel due to a tunnel construction is predicted with the help of input variables that have direct physical significance. The RF-based model is developed by free R programs, trained and tested with parameters obtained from the detailed investigation of different tunnel projects published in literature. The maximum settlement is taken as a function of tunnel diameter, depth to the tunnel axis, cohesion, internal friction angle, compressibility modulus of soil, grouting pressure, percent tail voild grout filling, thrust force and advance rate for shield tunneling. A repeated 5-fold cross-validation procedure (10 repeats) is implemented to determine the optimal parameter values during modeling and an external testing set is employed to validate the prediction performance of models. Two performance measures namely R2 and RMSE have been employed. The RF demonstrated a promising result and predicted the desired goal fairly successfully.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".