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Record W4406388460 · doi:10.1139/cgj-2024-0576

Time-dependent interaction coefficients to quantify the settlement of energy pile groups

2025· article· en· W4406388460 on OpenAlexvenueno aff
Filomena de Silva, Chiara Iodice, Gianpiero Russo

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPileGeotechnical engineeringSettlement (finance)Environmental scienceGeologyForensic engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

The use of energy piles is facing an exponential growth due to the increasing need of exploiting sustainable energy sources. Their design implies the estimation of the displacement induced by the combined thermo-mechanical interaction between piles belonging to the same group. In this regard, the work at hand derives sets of interaction coefficients for piles subjected to thermal load in not stationary conditions, using finite difference numerical analyses. These involved pile pairs embedded in different soil types at multiple spacing, namely 2, 4, 6, 8, 10 times the pile diameter. A new dimensionless parameter is also introduced to describe the evolution with time of the interaction coefficients accounting for pile-to-pile spacing, soil thermal diffusivity, pile–soil stiffness ratio, and time. The numerical results demonstrate a strict time-dependence of the interaction coefficients that is worthy to be considered in the design practice. To this aim, a practice-oriented approach is proposed allowing to evaluate the interaction effects and thereby derive the settlement of each pile in a straightforward manner. This requires as only ingredient the settlement of the isolated energy pile at the considered time instant. The simplified approach is successfully validated against full 3D thermo-mechanical numerical analyses on pile groups including conventional and energy piles with different layouts.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.969

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 head0.011
GPT teacher head0.249
Teacher spread0.238 · 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 designNot applicable
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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