Application of Signal Imaging Analysis Technology in Prediction and Treatment of Water Inrush in Diversion Tunnel
Bibliographic record
Abstract
A signal imaging analysis technology was proposed to accurately interpret geological radar detection images in order to address the issues of difficult interpretation of radar advance forecast images of water inrush in diversion tunnels in unfavorable geological zones and difficult detection of grouting effects in grouting circles. The waveform, amplitude, and frequency differences in the radar data among various geological bodies in the fracture development zone, broken zone, and water-rich zone can be analyzed by the signal imaging analysis technology, which can extract multiple technical parameters for comprehensive judgment and provide a foundation for the interpretation of geological radar images. In this study, signal mapping analysis technology was used to interpret the geological detection images taken in front of the tunnel face and the surrounding rock geological detection map after cement-polyvinyl alcohol grouting, respectively. The accuracy of the signal mapping analysis technology was confirmed, and the following conclusions were drawn: (1) Geographic Different geological structures, such as fissure zones, broken zones, and water-rich zones, have different reflection signal properties for radar electromagnetic waves. With the help of the image, distinct geological features can be identified and water inrush can be anticipated; (2) Electronic scanning imaging can be used to observe it. The geological radar image feedback of the grouting circle after grouting indicates that the lithology of the grouting circle is complete and the grouting reinforcement and sealing effect is good when the cement-polyvinyl alcohol slurry concretion particles are dense; (3) The numerical analysis results of the seepage field of the tunnel demonstrate that the grouting of the surrounding rock can effectively reduce water seepage and control water gushing. The study's findings offer a specific reference point for the forecasting and management of water gushing in diversion tunnels located in adverse geological regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".