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Record W2932046861 · doi:10.11159/icgre19.167

Experimental Study of Load Settlement Behaviour of Ring Footings forDifferent Internal Diameter Keeping the Contact Surface Area Same

2019· article· en· W2932046861 on OpenAlexvenueno aff
Hitesh Rupani, Manas Kumar Bhoi

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)Geotechnical engineeringSurface (topology)Materials scienceStructural engineeringRing (chemistry)GeologyEngineeringComputer scienceGeometryMathematics

Abstract

fetched live from OpenAlex

In some practical cases during the design of a foundation, ring configuration can be more preferred in comparison to the solid circular footing due to some field or topographical constraints.To address such issues and to reach a viable solution an experimental program was undertaken.This paper depicts the experiments conducted to study the load-settlement behaviour of ring footings on granular soil; under axial loading condition through Plate Load Test.The chosen ring footing was the special cases of the solid circular footing (Diameter 150mm); both having same contact area.The ring diameters chosen to achieve the same surface area are (in terms of Dinner-Douter) 60-160mm, 100-180mm, 200-250mm.The ring Diameter ratio for each are n=0.375,0.555, and 0.8 respectively.The Objective was to analyze the effect of increasing ring diameter ratio on the load-settlement behaviour while keeping the contact surface area same.The results show that bearing capacity of footing starts decreasing reasonably up to n=0.375 and afterward there is a steep decrease for n=0.555 and n=0.8.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.213
Teacher spread0.205 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicEngineering Structural Analysis MethodsFrench-language works237,207