Predicting Shear Behaviour of Joints Using Multi-Sensor and Manual Core Logging Data and Supervised and Unsupervised Learning Methods
Bibliographic record
Abstract
Joint surface characteristics significantly influence the shear behavior of discontinuity and the geomechanical design of a surface and underground excavation. Conventional core logging method for joint surface characterization is subjective and inconsistent. Sensor-based technologies can be adopted for surface characterization.This thesis compares manual and multi-sensor joint surface characterization to demonstrate the discrepancy between the manual and digital logging techniques. Joints detected over more than 500 meters of core samples were logged. A handheld 3D scanner, hardness test, and pXRF were performed on the joints to measure roughness, joint wall hardness, and a proxy for alteration mineralogy at discontinuity surface, respectively. Ja and Jr were assigned to the joints based on observations. Unsupervised learning techniques were used to classify the joints based on their surface characteristics. Samples from each class were selected for the direct shear test. Finally, unsupervised and supervised learning techniques were applied to predict joints' shear behaviour.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".