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Record W4410207586 · doi:10.1103/physreve.111.l053501

Nucleation-percolation transition in desiccation cracking of clay

2025· article· en· W4410207586 on OpenAlexaff
Yu-Han Yang, Chao Zhang, Hyoungsoo Kim, Renpeng Chen

Bibliographic record

VenuePhysical review. E · 2025
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsKootenay Association for Science & Technology
FundersNational Natural Science Foundation of China
KeywordsDesiccationNucleationPercolation (cognitive psychology)CrackingMaterials scienceComposite materialThermodynamicsEcologyPsychologyPhysicsBiologyNeuroscience

Abstract

fetched live from OpenAlex

Crack formation plays a critical role in both natural and engineered materials, influencing the structural integrity and failure of materials from soils to buildings. In this study, the clay desiccation cracking is observed to exhibit a transition of cracking mode from nucleation through avalanche to percolation. This cracking mode transition is dictated by the strain field disorder, which is deliberately regulated by changing the pore structure heterogeneity, with nucleation dominating at low disorder and percolation emerging at higher disorder levels. These transitions are captured by the fractal dimension of the sample-spanning crack, with ∼0.99 for nucleation, ∼1.72 for percolation, and values in between for avalanche, maintaining universality among the four types of clay. Additionally, the fractal dimension increases with strain field disorder in avalanche mode, which can be well captured by a logarithmic relation derived via the fractal tree model. Moreover, this logarithmic relation is universal among both the experimental data of the four types of clay and the numerical data of the classical fuse model. Our findings enhance the understanding of how material heterogeneity governs crack propagation, providing valuable insights for predicting and controlling fractures in natural and industrial processes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.280
Teacher spread0.272 · 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 teacher head, 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

Citations2
Published2025
Admission routes1
Has abstractyes

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