Research on the Law of Large-Scale Deformation and Failure of Soft Rock Based on Microseismic Monitoring
Bibliographic record
Abstract
Based on the existing Canadian ESG microseismic monitoring system, a mobile microseismic monitoring system for a soft rock tunnel has been successfully constructed through continuous exploration and improvement to study the large‐scale nucleation and development of microfractures in the soft rock of the Yangshan Tunnel. All‐weather, continuous real‐time monitoring is conducted while the tunnel is excavated through drilling and blasting, and the waveform characteristics of microseismic events are analysed. Through the recorded microseismic monitoring data, the variation characteristics of various parameters (e.g., the temporal, spatial, and magnitude distributions of the microseismic events, the frequency of microseismic events, and the microseismic event density and energy) are separately studied during the process of large‐scale deformation instability and failure of the soft rock tunnel. The relationship between the deterioration of the rock mass and the microseismic activity during this failure process is consequently discussed. The research results show that a microseismic monitoring system can be used to detect precursors; namely, the microseismic event frequency and energy both will appear “lull” and “active” periods during the whole failure process of soft rock tunnel. Two peaks are observed during the evolution of failure. When the second peak occurs, it is accompanied by the destruction of the surrounding rock. The extent and strength of the damage within the surrounding rock can be delineated by the spatial, temporal, and magnitude distributions of the microseismic events and a microseismic event density nephogram. The results of microseismic analysis confirm that a microseismic monitoring system can be used to monitor the large‐scale deformation and failure processes of a soft rock tunnel and provide early warning for on‐site construction workers to ensure the smooth development of the project.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".