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Record W2966109089 · doi:10.18280/rcma.290208

A 1D Compression Model for Loess Based on Disturbed State Concept

2019· article· fr· W2966109089 on OpenAlexvenueno aff
Yali Xu

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2019
Typearticle
Languagefr
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsnot available
FundersDivision of Undergraduate Education
KeywordsLoessCompression (physics)State (computer science)GeologyGeotechnical engineeringEnvironmental scienceComputer scienceSoil scienceGeomorphologyMaterials scienceAlgorithm

Abstract

fetched live from OpenAlex

Despite being insufficiently compacted, the loess structure can withstand a huge load like the super solid.This paper attempts to disclose how water content affects the bonding and arrangement of loess particles, and how it disturbs the loess structure.For this purpose, five confined compression tests are designed for intact loess and remolded loess of different water contents.Specifically, the disturbance function was defined with void ratio as the parameter based on the disturbed state concept (DSC) theory, considering how the structural features of loess Q3 affects the compressive strength.Next, the evolution law of the disturbance function was explored, and the influence law of water content on the parameters of the disturbance function was investigated.Finally, a DSC-based 1D compression model was constructed for loess Q3.The research results show that the water content has a great impact on the evolution law of the 1D disturbance function and its two parameters, and the influence can be described by exponential function.Besides, the experimental results show that our model can accurately describe how water content affects loess compression.

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)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.068
GPT teacher head0.299
Teacher spread0.230 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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Same venueRevue des composites et des matériaux avancésSame topicImage Processing and 3D ReconstructionFrench-language works237,207