Integrating rock mechanics and structural geology in rock engineering
Bibliographic record
Abstract
Abstract One of the major challenges facing rock engineers is that of establishing the bulk properties of the fractured rock mass on which or in which they are working. These are controlled principally by the geometry of the fracture network and the properties of the individual fractures. The network is built up by the superposition of separate fracture sets, each related to a geological event (burial tectonism and exhumation). In structural geology ‘fracture analysis’ is used to determine the order in which the sets are superimposed and knowing this, the 3D geometry of the network can be determined. Examination of the fracture surfaces can also reveal whether they are shear or extensional. Provided with this information the rock engineer can then combine it with site specific tests on the properties of the individual fracture sets and begin to quantify the likely physical behaviour of rock masses on an engineering scale. This paper presents a brief introduction to the concepts of fracture analysis, and goes on to show how these can usefully by integrated with typical rock mechanics analyses to give improved data for rock engineering design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".