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Record W7056007011

Digital image-based computational approaches for three-dimensional microstructure characterization

2019· article· en· W7056007011 on OpenAlexaboutno aff

Bibliographic record

VenueSMARTech Repository (Georgia Institute of Technology) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsTortuositySphericityPlanarMicrostructureShearing (physics)Characterization (materials science)Node (physics)Medial axisGranularityNetwork topology
DOInot available

Abstract

fetched live from OpenAlex

Employing multiscale approaches provides an innovative solution to advancing the understanding of macro-geomechanical phenomena by capturing quantitative structure information of geomaterials at the particle-pore scale. In the last two decades, microstructural properties of Ottawa sands subjected to laboratory biaxial and triaxial compression testing have been analyzed at Georgia Tech so that their correlation with the mechanisms of strain localization could be explored. Extensive 2-D and some preliminary 3-D knowledge and insights into the inherent variation and evolving behavior induced by shearing in unconsolidated sand microstructures were learned. Aimed at enhancing and complementing these preceding studies, this research focuses on developing appropriate computational methods for 3-D microstructure characterization, with a particular focus on examining the geometry and topology of the highly intricate continuum pore space. Under a proposed skeleton-based framework, the tortuous nature of pore structure is investigated through characterizing spatial variation of geometrical tortuosity using a novel, generic computational algorithm. Based on specifying and identifying pore throats from the pore skeleton, the physically representative network architecture of pore structure is established. A local sphericity algorithm and a planar surface construction algorithm are introduced to construct pore throats and extract network statistics. The effectiveness of these pore structure analysis tools is evaluated and demonstrated on two simulated idealized packing structures. For the particle phase, image-based separation and size measurements are conducted via morphological watershed processing. The topology of the particle network is represented by the distribution of force-chain tortuosity. Design-based stereological techniques for unbiased sampling and estimation are adopted to guarantee the quantitative analyses can be performed in a scientific manner, independent of the operator. All the developed methods and tools are applied to characterize three pairs of reconstructed 3-D digital Ottawa sand microstructures, including one pair for biaxial specimens and two for triaxial specimens. Shear-induced alternations in pore structure and particle network are examined from the comparative studies between the sheared microstructure and the unsheared counterpart of each pair, as well as inside and outside the shear zone for the sheared biaxial microstructure. Variations in the inherent structures are analyzed by comparing unsheared triaxial specimens prepared with air pluviation and moist tamping methods. In the characterization of true pore morphology, the encountered geometric complications and then high computational expense highlight the difficulty and challenge of creating a unique pore network for unconsolidated porous media systems.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.008
GPT teacher head0.210
Teacher spread0.202 · 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 designSimulation or modeling
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

Citations1
Published2019
Admission routes1
Has abstractyes

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