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Record W4309324288 · doi:10.1186/s40703-022-00176-5

Comparative analysis of international codes of practice for pile foundation design considering negative skin friction effect

2022· article· en· W4309324288 on OpenAlexaboutno aff
Assel Zhanabayeva, Shynggys Abdialim, Alfrendo Satyanaga, Jong Kim, Sung-Woo Moon

Bibliographic record

VenueInternational Journal of Geo-Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNazarbayev University
KeywordsPileFoundation (evidence)EurocodeEngineeringBridge (graph theory)Structural engineeringCivil engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Negative skin friction (NSF) effect on pile foundation design attracts the attention of geotechnical engineers and requires comprehensive research. However, the ways of consideration of NSF for the pile foundation design vary with different design codes. This study aims to compare design code requirements adhering to Construction Codes and Regulations (i.e., SNiP), Eurocode 7 (EC7), Canadian Highway Bridge Design Code (CHBDC), and AASHTO for the pile foundation design subjected to NSF and provide insights for the understanding effect of NSF on the behavior of pile foundation. The overall objective is achieved by designing three cases of a driven pile for given design conditions in Astana city. Then, a sensitivity analysis was performed to estimate the effects of the dimensions of the pile foundation and the shear strength of soils on the bearing resistance. The comparative analysis shows that pile foundation design adhering to CHBDC and AASHTO results in a more conservative strategy than SNiP and EC7 when considering Kazakhstani soil engineering conditions.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.785
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.276
Teacher spread0.261 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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