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Record W3101761943 · doi:10.11159/iccste20.205

Performance of Pile Cap Foundation with Respect to Cap Thickness

2020· article· en· W3101761943 on OpenAlexaffvenue
Soukayna El Hammouli, Adel Hanna, Lan Lin, Mahmoud Khalifa

Bibliographic record

VenueProceedings of the International Conference on Civil, Structural and Transportation Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsConcordia University
Fundersnot available
KeywordsFoundation (evidence)Pile capPileGeologyGeotechnical engineeringComputer scienceMaterials scienceGeography

Abstract

fetched live from OpenAlex

The thickness of a pile cap is a governing parameter in the design of pile-cap foundations.A thin cap perform as a flexible slab which distributes the load on the piles unevenly, and it does not accord with the concept of pile foundation design.On the other hand, a thicker and rigid cap is expected to evenly distribute the load on the piles, as well as resisting bending moments and punching shear failure, but may impose more load on the foundation compared to a thinner cap.In view of these differences, there is a need to determine how the pile cap thickness would affect the performance of piles and pile cap.For this purpose, a 3-D finite element model was developed to simulate a 16-pile cap foundation for a range of cap thickness from 0.5m to 3m.The collected data is in the form of the load on individual piles and the deformation of the cap.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.204
Teacher spread0.191 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes2
Has abstractyes

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Same venueProceedings of the International Conference on Civil, Structural and Transportation EngineeringSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207