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Record W3116929558 · doi:10.1139/cgj-2019-0236

The mechanisms underlying long-term shaft resistance enhancement of energy pile in clays

2020· article· en· W3116929558 on OpenAlexvenueno aff
Saeed Yazdani, Sam Helwany, C. Guney Olgun

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
FundersDivision of Civil, Mechanical and Manufacturing InnovationNational Science Foundation
KeywordsStiffeningPileGeotechnical engineeringStiffnessMaterials scienceConstant (computer programming)Interface (matter)Stress (linguistics)Foundation (evidence)GeologyComposite materialStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Although there are several studies indicating that heating increases the long-term shaft resistance of energy piles, the mechanisms by which heating causes this increase have not been adequately evaluated yet. This article presents a comprehensive analysis and discussion to assess the important factors contributing to this increase by integrating the findings from three recently published papers studying the thermo-mechanical behavior of clay and the clay–pile interface. In these three studies, reconstituted kaolin clay was used, and cyclic and monotonic heat ranging between 24 and 34 °C were applied to the clay and interface. The interface was sheared under two stiffness boundary conditions: constant normal stiffness (CNS) and constant normal load (CNL), where normal stresses varied between 100 and 300 kPa. The analysis presented in this article reveals that the increase in strength of the interface under the CNL condition is primarily attributed to clay stiffening at the interface. However, the increase in shaft resistance under the CNS condition is primarily attributed to the heating-induced increase of effective lateral stress, although clay stiffening at the interface also partially contributes to the total increase of shaft resistance.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.979

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 science0.0010.000
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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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 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

Citations9
Published2020
Admission routes1
Has abstractyes

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