Detection and stiffness measurement of weak zones in cement-treated ground using travel-time tomography
Bibliographic record
Abstract
Cement-treated ground often possesses significant spatial variation in strength and stiffness. Studies on the detection and stiffness measurement of weak zones in cement-treated ground using seismic geophysical methods remain limited to date. The work examines the feasibility of using seismic travel time tomography to detect and measure the stiffness of weak zones in cement-treated ground, through 1-g modelling of a cross-hole setup with weak zones of prescribed sizes and stiffnesses. Using bender elements as transmitters and receivers, shear wave velocity profiles across the weak zones are mapped. Sizes, locations, and stiffnesses of the weak inclusions are also inferred from shear wave travel times using GeoTomCG. The results indicate that although the sizes and locations of weak zones can be reliably detected using first-arrival time-picking, the stiffnesses is significantly over-estimated. The latter is due to the arrival of the diffracted waves around the weak zone masking the arrival of the transmitted waves. A modified time-picking method, using the wavelet transform, is developed and is shown to give more reliable stiffness measurements of the weak zone, but not the shape. Combining direct and wavelet time-picking allows the sizes, shapes, locations, and stiffnesses of the weak zones to be more reliably resolved.
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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 | 0.000 |
| 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".