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Record W3013266728 · doi:10.1139/cjce-2019-0735

Analysis of moisture damage susceptibility of semi-rigid base materials based on hydrodynamic pressure erosion test and damage constitutive model

2020· article· en· W3013266728 on OpenAlexvenueno aff
D. Sha, Baofeng Pan, Yiren Sun, Tiankai Che, Jiale Lu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsMoistureErosionBase (topology)Geotechnical engineeringStructural engineeringMaterials scienceMechanicsGeologyComposite materialEngineeringMathematics

Abstract

fetched live from OpenAlex

The hydrodynamic erosion generated by the pumping action of the wheel load during rain and snow is the main cause of the semi-rigid base failure. Unfortunately, however, very few experimental methods and damage models can reasonably describe and predict the moisture damage performance of semi-rigid base materials under the pumping action. To address this issue, a hydrodynamic pressure erosion test was conducted firstly. Then, the compressive test was performed on the eroded specimens to evaluate the damage mechanism of semi-rigid base materials subjected to hydrodynamic erosion. Finally, a moisture-induced damage model that is applicable to the semi-rigid base materials was established by the concept of damage mechanics. The results indicated that the presented damage model encapsulates the complex behavior of the semi-rigid base materials under the hydrodynamic pressure and provides a more rational method for predicting the moisture damage performance of semi-rigid base materials.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.988

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)0.0010.000

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 head0.008
GPT teacher head0.192
Teacher spread0.185 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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