MétaCan
Menu
Back to cohort
Record W4403471081 · doi:10.1680/jgele.24.00025

State parameter predictions based on cone penetration test simulated with MPM: an application to tailing deposits

2024· article· en· W4403471081 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

VenueGéotechnique Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Mechanics
Canadian institutionsCarleton University
Fundersnot available
KeywordsCone penetration testPenetration (warfare)Penetration testGeologyEnvironmental scienceGeotechnical engineeringMaterials scienceEngineeringOperations research

Abstract

fetched live from OpenAlex

The ‘state parameter’, which compares the current void ratio with the critical state void ratio, plays a crucial role in quantifying sandy soil behaviour. In situ methods, such as cone penetration tests (CPTs), can quantify the mechanical state of sand. However, establishing a direct relationship between cone resistance and the state parameter requires a complex back-analysis of the processes occurring in the soil during the test. Currently, a cavity expansion solution is being used to relate the state parameter to the cone resistance, necessitating the use of a calibrated scaling equation. In this study, 600 material point method CPT simulations are performed – which employ the critical state NorSand model – to derive a direct predictive equation for estimating the state parameter from CPTs. This eliminates the need for a scaling equation. The predictive equation computes cone resistance as a function of the NorSand parameters and the state parameter of the soil. The accuracy of this predictive equation is subsequently evaluated by comparing the results against the chamber test data of tailings deposits, showing promising performance.

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: none
Teacher disagreement score0.846
Threshold uncertainty score0.770

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 head0.005
GPT teacher head0.199
Teacher spread0.195 · 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