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Record W2770594836

Stress Estimation From Borehole Scans For Prediction Of Excavation Overbreak In Brittle Rock

2017· dissertation· en· W2770594836 on OpenAlexfundaboutno aff
Andrew LeRiche

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaStrongGovernment of OntarioNuclear Waste Management Organization
KeywordsBoreholeGeologyExcavationGeotechnical engineeringBrittlenessStress (linguistics)Rock mechanicsSeismologyMining engineeringMaterials scienceMetallurgy
DOInot available

Abstract

fetched live from OpenAlex

In the field of geomechanics, one of the most important considerations during design is the state of stress that exists at the location of a project. Despite this, stress is often the most poorly understood site characteristic, given the current challenges in accurately measuring it. This stems from the fact that stress can’t be directly measured, but must be inferred by disturbing the rockmass and recording its response. Although some methods do exist for the prediction of in situ stress, this only provides a point estimate and is often plagued with uncertain results and practical limitations in the field. This research proposes a methodology of continuously predicting stress along a borehole through the back analysis of borehole breakout and how this same approach could be employed to predict excavation overbreak. \n\tKGHM’s Victoria Project in Sudbury, Canada, was the location of data collection, which firstly involved site characterization through common geotechnical core logging procedures and laboratory scale intact core testing. Testing comprised Brazilian tensile strength and unconfined compressive strength testing, which involved the characterization of crack accumulation in both cases. \n\tFrom two pilot holes, acoustic televiewer surveys were completed to characterize the occurrence and geometry of breakout. This was done to predict the orientation of major principal stresses in the horizontal axis, with the results being further validated by the geometry of stress-induced core disking. From the lab material properties and breakout geometries, a continuum based, back analysis of breakout was done through the creation of a generic database of stress dependent numerical models. When compared with the in situ breakout profiles, this created an estimate of stress as a function of depth along each hole. The consideration of the presence of borehole fluid on the estimate of stress was also made. This provided the upper-bound estimate of stress from this methodology. Given the generic nature of the numerical models, potential shaft overbreak was also assessed using this technique and from the previously described estimate of stress.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.173
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes2
Has abstractyes

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