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Mode of Failure of a Group of Stone Columns in Soft Soil

2013· article· en· W2045707322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Geomechanics · 2013
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsGolder Associates (Canada)Concordia University
Fundersnot available
KeywordsShearing (physics)Column (typography)Structural engineeringFailure mode and effects analysisGeotechnical engineeringParametric statisticsPunchingGroup (periodic table)EngineeringMathematicsMechanical engineering

Abstract

fetched live from OpenAlex

The design of stone columns is currently based on theories developed for a single column, ignoring the group interaction and therefore the group efficiency. Whereas single stone columns mainly fail by bulging, a group of stone columns may fail by bulging or shear of the entire soil/columns mass. A numerical model was developed to simulate the case of a single stone column and a group of stone columns installed in soft clay. The model establishes the level of interaction between individual columns and therefore determines the mode of failure of a given geometry, soil, and loading condition. The model was validated with the available experimental data in the literature and used to generate data for a potential mode of failure. This parametric study is conducted to examine the effect of the parameters believed to govern the mode of failure and includes modulus of elasticity of the stone column material and the clay, column diameter, and the spacing and angle of shearing resistance of the column material. The design procedure is presented to assist the designer in establishing the mode failure of a group of stone columns (bulging, general, local, or punching shear).

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.

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 score0.236
Threshold uncertainty score0.308

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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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