Risk Assessment System Based on Fuzzy Composite Evaluation and a Backpropagation Neural Network for a Shield Tunnel Crossing under a River
Why this work is in the frame
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Bibliographic record
Abstract
Constructing a shield tunnel that crosses under a river poses considerable safety risks, and risk assessment is essential for guaranteeing the safety of tunnel construction. This paper studies a risk assessment system for a shield tunnel crossing under a river. Risk identification is performed for the shield tunnel, and the risk factors and indicators are determined. The relationship between the two is determined preliminarily by numerical simulation, the numerical simulation results are verified by field measurements, and a sample set is established based on the numerical simulation results. Fuzzy comprehensive evaluation and a backpropagation neural network are then used to evaluate and analyze the risk level. Finally, the risk assessment system is used to evaluate the risk for Line 5 of the Hangzhou Metro in China. Based on the evaluation results, adjustments to the slurry strength, grouting pressure, and soil chamber pressure are proposed, and the risk is mitigated effectively.
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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 it