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Record W3017822431 · doi:10.33430/v27n1thie-2017-0052

Shear strength properties of Hong Kong soils for slope stability

2020· article· en· W3017822431 on OpenAlexaff
Hong Yau Wong

Bibliographic record

VenueHKIE Transactions · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsGeotechnical engineeringStandard penetration testTriaxial shear testDirect shear testShear strength (soil)Shear (geology)Soil testGeologyLaboratory testPenetration testDrillSoil waterEngineeringSoil scienceMechanical engineeringLiquefaction

Abstract

fetched live from OpenAlex

With the recent advancement in technology, the method, accuracy and speed of slope stability analysis have been vastly improved. Nevertheless, the reliability and appropriateness of such analysis can be very much in doubt if the soil behaviour, in particular the shear strength behaviour, is not fully understood. The objective of this paper is therefore to evaluate the shear strength behaviour of various soil types in Hong Kong. This comprises the collection, processing and analysis of the laboratory and field works carried out in the past few decades, in particular the triaxial compression testing in the laboratory and standard penetration test (SPT) in the field. For correlating SPT with soil shear strength, a fairly large number of carefully controlled site investigation works with SPT are carried out both above and below any Mazier sampling. Laboratory triaxial tests are then carried out in these Mazier samples. Finally, it is hoped that a basically quantitative relationship between soil shear strength and SPT can be obtained as this would enable a soil shear strength profile to be established once the corresponding SPT profile has been determined on site by carrying out SPT in adequate number of drill holes.

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: none
Teacher disagreement score0.833
Threshold uncertainty score0.404

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 head0.035
GPT teacher head0.198
Teacher spread0.164 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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