Discrete fracture network application to rock slope engineering
Bibliographic record
Abstract
The stability of rock slopes is a critical concern in rock engineering applications due to the potential safety hazards and economic repercussions associated with slope failures. Currently, slope stability analysis relies on various analytical and numerical modeling tools leading to a factor of safety. Parameters such as joint strength are assessed through probabilistic analysis. Despite the established effectiveness of these methods, they often lack the capability to provide a volumetric estimation of the failure zone, primarily because the joint frequency parameter is not considered. By incorporating this parameter, it becomes feasible to construct a three-dimensional discrete fracture network (DFN), which offers a more comprehensive representation of the rock slope structure. This study aims to validate the use of DFN in civil engineering applications using two road-cut case studies from Saudi Arabia. Both case studies are analyzed with the conventional methods and DFN approaches, allowing for a comparative assessment of results. DFN models optimize the utilization of statistical data related to discontinuity persistence and spacing, enabling the construction of both deterministic and stochastic fracture networks. The effects of joint spacing and persistence on the factor of safety and volume of failure are examined. Finally, the advantages and limitations of the DFN on rock slope stability are highlighted and areas for improvement and optimization are discussed.
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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".