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Record W4385629099 · doi:10.32920/23899827

Inverse analysis on compressibility of Toronto clays

2023· preprint· en· W4385629099 on OpenAlexafffundabout
Abulimilti Ayizula, Jinyuan Liu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOedometer testCompressibilityInverseGeotechnical engineeringSoil waterSoil testMathematicsSoil scienceGeologyPhysicsThermodynamicsGeometry

Abstract

fetched live from OpenAlex

This study investigated the compressibility of clay deposits in Toronto, Canada, through an inverse analysis. A series of oedometer test results from a local transit project were collected and used to calibrate the stiffness parameters according to the Hardening Soil (HS) model. The inverse analysis was conducted by coupling UCODE, a universal inverse modeling tool, and PLAXIS to fit the simulated stress–strain curves to the test results. First, a sensitivity analysis was performed to identify the most critical model parameters and simplify the problem by removing less sensitive parameters. Second, the selected HS model parameters were calibrated for a total of 71 oedometer tests conducted on different soil types. Third, a statistical analysis was performed on the calibrated HS model parameters according to soil type. In the end, a series of prediction formulas were derived to estimate the compressibility parameters from soil index properties and standard penetration test measurements, which can be used to better simulate the deformation behavior of local soils in practice.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.672

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.258
Teacher spread0.227 · 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 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

Citations0
Published2023
Admission routes3
Has abstractyes

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