Conceptual Analog for Evaluating Empirically and Explicitly the Evolving Shear Stress Along Active Rockslide Planes Using the Complete Stress–Displacement Surface Model
Bibliographic record
Abstract
The stability analysis of rock slopes traditionally involves the evaluation of limit state conditions to determine the potential for rockslides and rockfalls. However, empirical evidence supported by experimental studies has highlighted the complex response of rock interfaces under differential loading. It is characterized by distinct pre-peak and post-peak stress–deformation relationships, which represent the deformation profile of loaded rock interfaces and, thus, capture dynamic and evolving events. The present research introduces an interpretation framework to reconcile these contradicting paradigms by interpreting empirically and explicitly the full stress–displacement relationship along active shear surfaces of rockslide events. The Complete Stress–Displacement Surface (CSDS) model was incorporated into conventional analytical solutions for a rock slope planar failure to describe the evolving stress conditions during an active rockslide event. The Ruinon rockslides (Italy), monitored and studied extensively at the turn of the century, are revisited using the adapted CSDS model to describe the evolving stress–deformation conditions. Empirical and experimental calibrations of the model are implemented and compared using the CSDS model for the description of evolving shear stresses in large rockslide events based on topographical monitoring. This paper contributes a detailed framework for correlating in situ topographical monitoring with relevant geomechanical information to develop a representative model for the evolving stress conditions during a rockslide event.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".