Study on the seismic damage and dynamic support of roadway surrounding rock based on reconstructive transverse and longitudinal waves
Bibliographic record
Abstract
The magnitude and frequency of induced seismicity increase as mining excavation reaches greater depth, leading to the increasingly severe damage to roadways caused by high-energy seismic waves. To comprehensively simulate the damage caused by dynamic loads, a synchrosqueezing transform and empirical mode decomposition method was developed, which effectively decomposed raw seismic wave signals into transverse and longitudinal components. This novel method produced more accurate results in terms of velocity, displacement, rock yielding patterns, and reflecting theoretically orthogonal oscillating directions of transverse and longitudinal waves compared to using raw mixed waves at the seismic source. Under the disturbance of transverse and longitudinal waves, the vertical displacement was much higher than horizontal displacement at the top position of the roadway, while the horizontal displacement was greater at the sidewalls. The particle vibration velocity, displacement and yielding zone of the surrounding rock of roadway were proportional to the energy level of seismic, while inversely proportional to the source-roadway distance. The proportion of damage attributed to transverse waves increased with the energy level, ranging from 75.8% to 85.8%. Eventually, a roadway dynamic support design was optimized based on the proposed seismic wave processing and modeling methodology. The methodology offers guidance for roadway dynamic support design, with the goal of averting excessive or insufficient support strength. Document Type: Original article Cited as: He, S., Shen, F., Chen, T., Mitri, H., Ren, T., Song, D. Study on the seismic damage and dynamic support of roadway surrounding rock based on reconstructive transverse and longitudinal waves. Advances in Geo-Energy Research, 2023, 9(3): 156-171. https://doi.org/10.46690/ager.2023.09.04
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".