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Record W2928393109 · doi:10.11159/icgre19.140

A Comparison Study: Estimating the Axial Micropiles Capacity Using Current Practices

2019· article· en· W2928393109 on OpenAlexvenueno aff
Ahmed Elgamal

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsCurrent (fluid)Computer scienceGeologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

According to the innovation of new techniques used in building constructions, a new structural case of loading appears. An additional usage of micropiles beside the conventional purpose is needed to cope with these cases of loading. Nowadays, retrofit buildings to resist static and dynamic construction loadings is an urgent need. Due to the uneconomic of conventional piles or the restricted construction of retrofitting buildings, or to reinforce the weak soils, micropiles are appropriate to be used. Although, up to now, there is no accurate estimation of the axial capacities of micropiles (i.e., compression and tension). Current design guidelines try to introduce safe and economic methods of estimating the axial capacities of micropiles. All these guidelines recommend just preliminary designs and performing a full-scale test is a must to validate the designed capacity. A comparison study is presented in this paper to catch on the most suitable method should be used to estimate the axial micropile capacities by comparing the results to full-scale tests results. The end bearing can be neglected in the micropile design in case of the need to limit the building settlement. Some engineering design standards can be applied to design micropiles and more investigation is needed to enhance the tensile geotechnical capacity of micropiles.

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 score0.030
Threshold uncertainty score0.745

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)0.0000.000

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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

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