Integrating Stress Fracturing and Bulking Monitoring for Deformation-Based Ground Support Design Calibration in a Deep Caving Operation
Bibliographic record
Abstract
ABSTRACT: In a highly stressed environment, massive rock masses fail through stress fracturing, resulting in rock slabs or spalls that can detach from the excavation perimeter either progressively in a non-violent manner as spalling, or suddenly and violently in the form of strainbursting. Both phenomena ultimately lead to rock mass bulking near the excavation boundary that reduces support system capacity, creating safety risks for workers and costly interruptions in production. An innovative and recent approach to designing support in highly stressed brittle rock, known as deformation-based support design (DBSD), offers advantages in addressing the challenges associated with brittle failures around underground excavations. However, the limited availability of purpose-focused field data and systematic procedures for effectively utilizing monitoring techniques to determine DBSD's critical parameters often hinder its optimization, particularly in deep caving operations. This paper presents a systematic method and recommendations to improve design justification and optimization of the DBSD approach for the PT Freeport Indonesia (PTFI) Deep Mill Level Zone (DMLZ) panel cave mine. The study introduces a systematic process that integrates stress fracturing and bulking monitoring techniques, leveraging monitoring data to calibrate the critical parameters of the DBSD. A site-specific predictive function is proposed for estimating the depth of stress fracturing and the corresponding displacement based on the transient nature of the bulking factor across the extraction level footprint. Implementing this procedure into PTFI's DBSD tool effectively improves the forecasting of preventative support maintenance in areas with high demand, thereby promoting the integrity and reliability of the excavation. 1. INTRODUCTION Experiences gained from past and ongoing deep mining and caving operations suggest that brittle failure around underground excavations have been a significant challenge and contributes to hazards in deep underground operations. This phenomenon poses a safety risk for workers and can result in costly interruptions to production. Brittle failure occurs through stress fracturing. Fractures begin to form when induced stresses exceed the crack initiation strength of the rock (Martin, 1997; Kaiser et al., 2000). These fractures will propagate parallel to the maximum compressive stress and open perpendicular to the direction of minimum confinement. This opening mode distinguishes these fractures as extensional fractures, and differentiates them from shear fractures that develop under higher confinements and involve a shearing displacement mode. Thus, near the excavation boundary where confining stresses are low, the failure process is characterized by the formation of extensional fractures growing parallel to the excavation boundary. This, in turn, results in the creation of a set of rock slabs or spalls that can detach from the perimeter of the excavation, known as spalling. As the deviatoric stresses increase relative to the strength of the rock, it progresses deeper into the rock mass. However, as the spalling moves deeper into the rock mass away from the excavation, the higher confining stresses encountered start to suppress and limit the progression of extensional fracturing, resulting in the transition toward the formation of shear fracturing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".