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Record W4233754180 · doi:10.11159/icgre20.159

Model Test and Corresponding Simulation onCompressive Characteristics of Soilbags

2020· article· en· W4233754180 on OpenAlexvenueno aff
Tatsuya Doi, Yoshitaka MURONO, Ho Cho

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCompressive strengthTest (biology)Computer scienceMaterials scienceComposite materialGeology

Abstract

fetched live from OpenAlex

It is known that response acceleration of superstructure supported by spread foundation will decrease due to the rocking of the footing when subjected to earthquake loading, which makes it possible to reduce the cross section of the pier supporting the superstructure. However, supporting ground of a spread foundation should have enough bearing capacity. Therefore, when a spread foundation is constructed on soft ground, it should be improved by replacement of gravel or ground improvement method to increase the bearing capacity. The authors have already proposed a foundation by replacing the soft ground beneath a footing with soilbags. It is wellknown that soilbags will receive much larger compressive force than filled material only. Thus, it is expected that soilbags will withstand large load beneath the footing even during earthquake. In verifying the effectiveness of the proposed foundations, the compressive characteristics of soilbags is crucial. Therefore, in this study, a compression test on soilbags was conducted and the compressive characteristics of soilbags was carefully investigated. Moreover, numerical simulation of the compression test was carried out and the mechanism of compression deformation of soilbags was examined. The calculated results coincided almost exactly with the experimental results. The calculated results also showed that vertical stress was largely shared by the centre part of soilbags, where restraint effect of a bag was more prominent.

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 score0.264
Threshold uncertainty score0.662

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicSoil Mechanics and Vehicle DynamicsFrench-language works237,207