Development of a Non-Destructive Stress Measurement Technique with Applications in Deep Mining Using Distributed Sensing
Bibliographic record
Abstract
ABSTRACT: A crucial aspect of rock engineering design for deep mining projects is comprehending the in-situ stress field. This is particularly true for panel cave mining, where the excavation and support design of the extraction level tunnels and pillars must account for an initial stress increase related to their development and subsequently to the abutment stress resulting from undercutting and cave initiation. Thus, stress measurement should extend beyond establishing the pre-mining stress state to continuous monitoring of the evolving stress field, spatially and temporally, throughout operations. To do so accurately poses challenges; a lack of repeatability or permanency hinders existing methods. This paper introduces an innovative technique for stress measurement, merging borehole-distributed sensing and geophysics to address these challenges in deep mining. The technique aims to provide comprehensive, non-destructive measurements of stress magnitudes and orientations over large rock volumes, offering repeatable testing and monitoring opportunities. Feasibility and validation testing results showcase the potential benefits of this approach, emphasizing its role in enhancing in-situ stress understanding and management for optimized and safe deep mining and caving operations. 1. INTRODUCTION Engineering analyses for excavation stability and support design hinge on establishing specific boundary conditions. Among these, the in-situ stress state is paramount for rock engineering design of deep mining projects. The in-situ stress is a tensor quantity whose measurement poses a formidable challenge, often fraught with difficulty and reliability concerns. It is common for stress measurements to reveal conflicting stress interpretations, introducing considerable uncertainty and risk to deep mining projects. This inherent complexity frequently results in suboptimal design performance and costly errors. Various methods for stress measurement are in use, each presenting notable limitations and challenges. These are typically categorized into two methodologies, as outlined by Amadei and Stephansson (1997). The first involves measuring strain responses, such as strain relief from overcoring, allowing for the inversion of the in-situ stress field (Sjöberg et al., 2003). However, these measurements encounter reliability issues related to conformability, requiring meticulous preparation and a complete attachment of the measurement probe to the rock, and from being a point measurement, neglecting the inherent heterogeneity and anisotropy of rock and its effect on the strain response of the larger rock mass volume. The second type of methodology involves measuring the pressure necessary to initiate and/or maintain an open fracture of a specific orientation, often induced through hydraulic fracturing (Haimson & Cornet, 2003). Despite offering insights into larger rock volumes, the latter methods are inherently destructive, generating new fractures that prevent repeat validation testing over time. Additionally, the nature of these measurements and the effort required often limits them to a single point in time, restricting the ability to monitor stress changes over extended periods, a consideration particularly relevant in the dynamic environments of deep mining and panel caving.
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 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".