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Record W4322210168 · doi:10.5194/egusphere-egu23-14955

Equivalent Biot and Skempton coefficients for fractured rocks

2023· preprint· en· W4322210168 on OpenAlexaff
Silvia De Simone, Caroline Darcel, Hossein A. Kasani, Diego Mas Ivars, Philippe Davy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsNuclear Waste Management Organization
Fundersnot available
KeywordsBiot numberGeologyIsotropyScalingPoromechanicsFracture (geology)Pore water pressureMechanicsGeotechnical engineeringPorosityPorous mediumGeometryMathematicsPhysics

Abstract

fetched live from OpenAlex

Biot coefficient and Skempton coefficient are key descriptors of the coupled hydro-mechanical (HM) behavior of fluid-saturated porous materials. Biot coefficient defines a relationship between an applied load, fluid pressure and the stress that effectively acts on the solid skeleton. Skempton coefficient defines the temporary pore pressure variation caused by the application of a load in undrained conditions. The product of the two coefficients establishes the impact of an applied load on the solid skeleton, and thus the material deformation, under undrained conditions. The two coefficients are generally estimated through analytical expressions valid for isotropic homogeneous materials, or they are experimentally estimated at the laboratory sample-scale.In this work, we define a framework for the evaluation of equivalent Biot coefficient and Skempton coefficient at the scale of a fractured rock mass. We derive theoretical expressions that estimate the two equivalent coefficients from the properties of both the porous intact rock and the discrete fracture network (DFN), including fractures with different orientation, size, and mechanical properties. These formal expressions are validated against results from fully coupled hydro-mechanical simulations on systems with explicit representation of deformable fractures and rock blocks. We show that the coefficients largely vary with the fracture orientation and density, which implies that disregarding the presence of fractures may incur an incorrect evaluation of the HM response. We also discuss the variability of the coefficients under different settings of DFN properties, including realistic scaling conditions of size-dependent and stress-dependent fracture properties.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.276
Teacher spread0.232 · 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 designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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