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Record W4409360399 · doi:10.1139/cgj-2024-0491

The influence of soil layering and penetrometer diameter on penetration resistance

2025· article· en· W4409360399 on OpenAlexvenueno aff
Yishan Tian, Barry Lehane

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrometerLayeringGeotechnical engineeringPenetration (warfare)GeologySoil waterEnvironmental scienceSoil scienceEngineering

Abstract

fetched live from OpenAlex

Methods employing cone penetration test (CPT) data for site characterisation and foundation design have continued to evolve as use of the CPT grows worldwide. Research to assist the development of such methods has included penetration testing in a laboratory environment where the presence of thin soil layers in samples has highlighted the need for improved understanding of the influence of penetrometer size. This paper presents the results of a systematic experimental investigation of the relationship between the penetration resistance, penetrometer diameter, and relative strength of the soil layers in two-layered sand–sand and sand–clay profiles. The results combined with other high quality experimental results reported in the literature are used to quantify the influence of the strength of the layers on the cone resistance at the boundary of two layers as well as on the nature of the sensing and development sections of the cone profile. These observations inform modifications to the Boulanger and DeJong filtering method, with the primary objective of developing a consistent approach for the prediction of ultimate end bearing resistance of driven piles in layered stratigraphy. Examples using different diameter penetrometers in the field and in multi-layered deposits created in the laboratory illustrate the suitability of these modifications.

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 score0.254
Threshold uncertainty score0.844

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 head0.193
Teacher spread0.188 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicSoil Mechanics and Vehicle DynamicsFrench-language works237,207