Measuring Brittle Damage Thresholds of Crystalline Rocks by Acoustic Emissions on Compressive and Tensile Laboratory Tests
Bibliographic record
Abstract
ABSTRACT: The use of acoustic emission (AE) monitoring in brittle rock laboratory testing is one of the most accurate methods for measuring damage evolution and failure. In this study, AE data was collected from two separate laboratory testing programs. The first program consists of a series of unconfined compressive strength (UCS) tests completed on moderately foliated meta-sedimentary rocks from the Bathurst Mining Camp in New Brunswick, Canada, and high metamorphic grade tonalite gneiss from the Pointe du Bois pluton in Manitoba, Canada. The second program consists of a series of Brazilian tensile strength (BTS) tests completed on polymineralic homogenous and isotropic granites and amphibolite from the Pointe du Bois pluton. Using data collected by a Physical Acoustics Pocket AE system, individual waveforms are analyzed to distinguish tensile fracturing from shear fracturing events. Distinct trends and timing in the occurrences of tensile and shear fractures are observed leading to further insight into the behaviours of the materials throughout the loading process. This study provides enhanced guidelines for the collection of AE data and choice of instrumentation, filtration of measured events, and interpretation of filtered data to develop a greater understanding of the micromechanics occurring within these materials in both UCS and BTS tests. 1. INTRODUCTION Acoustic emissions (AE) are one of the most accurate measurement tools that can be used to study damage development in rock and concrete materials. Acoustic emissions are elastic vibrations emitted when microcracks form within a brittle material. For structural and tunnel stability, acoustic emissions can be used to assess strength and monitor the development of damage including microcrack formation, growth, and spalling. There are numerous studies examining acoustic emissions and microfracturing for homogenous unconfined compressive strength (UCS) tests such as Scholz (1968), Ohnaka & Mogi (1982), Lockner (1993), and Diederichs et al. (2004). There are few recent studies regarding AE in Brazilian tensile strength (BTS) tests, which presently include Keshavarz et al. (2008), Liu et al. (2015), Wang et al. (2019), and Khadivi et al. (2023). In much of this literature, acoustic emissions are used to quantify damage development. However, as examined in Diederichs et al. (2004), Ohtsu (2008), Shiotani (2008), and Ohno & Ohtsu (2010), AE can also be used to determine the type of damage occurring and how it impacts material strength. In this study, two (2) testing programs were completed; the first being a series of UCS tests on moderate to high grade metamorphic rocks and one crystalline rock, and the second being a series of BTS tests on low grade metamorphic rocks. Sample details are provided in Section 2, along with testing equipment specifications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".