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Record W1804997377 · doi:10.1520/stp14358s

Determination of Effective Stresses and the Compressibility of Soil Using Different Codes of Practice and Soil Models in Finite Element Codes

2000· book-chapter· en· W1804997377 on OpenAlexaff
PJ Blommaart, Peter The, J Heemstra, R J Termaat

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsCompressibilityFinite element methodGeotechnical engineeringStructural engineeringGeologyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Creep or secondary compression plays an important role in the deformation behavior of soft soils like peat and organic clay. For the determination of long term settlements several methods have been proposed and implemented in codes for the engineering practice. However, some of the methods used have been developed empirically. In this paper the effective stresses and the compressibility of soils are addressed for some practical cases using the methods mentioned and soil models in finite element codes. The conceptual differences in methods and calculation results are compared. A stress-strain-creep strain rate model is proposed (Plaxis Soft Soil Creep model) in which strain rates are coupled with stresses and strains. The proposed material creep model is based on the modified Cam-clay model, which has been extended with a viscoplastic formulation using a single creep parameter μ*. The compressibility parameters κ*, λ* and μ* used within the model can be determined by standard one-dimensional consolidation tests. Applying the different methods, the calculated settlements show differences in the ratio between the primary and secondary compression as well as the distribution in time of the excess pore pressures. For large settlements the introduction of the natural strain, defined as the strain related to the actual height instead of the initial height of the soil layer, gives a further improvement of the model proposed (a-b-c model).

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 score0.413
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.243
Teacher spread0.228 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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