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Record W2015086629 · doi:10.1139/t99-126

Modelling subsidence in the Hanoi City area, Vietnam

2000· article· en· W2015086629 on OpenAlexfundvenueno aff
Trinh Minh Thu, D. G. Fredlund

Bibliographic record

VenueCanadian Geotechnical Journal · 2000
Typearticle
Languageen
FieldEngineering
TopicSynthetic Aperture Radar (SAR) Applications and Techniques
Canadian institutionsnot available
FundersUniversity of Saskatchewan
KeywordsGroundwaterConsolidation (business)SiltGeotechnical engineeringAquiferSubsidenceGeologyInfiltration (HVAC)Human settlementHydrology (agriculture)Groundwater-related subsidenceAquifer propertiesEnvironmental scienceMining engineeringGroundwater rechargeStructural basinGeomorphologyGeographyArchaeology

Abstract

fetched live from OpenAlex

A study of land subsidence due to groundwater pumping in the city of Hanoi, Vietnam, was conducted by collecting and analyzing data on the geology, hydrology, soil properties, and observed settlements. The city of Hanoi is underlain by sediments consisting of organic and inorganic clays, silt, peat, sand, and gravel. The pumping of groundwater causes consolidation of compressible aquitard layers. The water demand for the city of Hanoi is increasing with time. The present total rate of water pumping is 450 000 m 3 /day, and there is a proposal to increase the rate to 751 000 m 3 /day by the year 2010. This research program involved the modelling of seepage related to pumping along with a stress-deformation analysis. The effect of surface infiltration was also modelled. The settlements computed for parts of the city of Hanoi were compared with measurements of settlement in the city area. The simulation results appear to be in fairly good agreement with the measurement results. The study showed that subsidence due to groundwater pumping is a serious problem in the city of Hanoi. It is important to continue to measure settlements and compute possible deformations associated with actual rates of pumping.Key words: subsidence, settlement, groundwater pumping, stress-deformation modelling, seepage modelling.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.869

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.209
Teacher spread0.193 · 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 designOther design
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

Citations55
Published2000
Admission routes2
Has abstractyes

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