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Record W4399872805 · doi:10.1139/cgj-2024-0058

Diaphragm wall lateral movement in deep excavations in Bangkok clays: impacts and influencing factors

2024· article· en· W4399872805 on OpenAlexvenueno aff
Thayanan Boonyarak, Aye Yadana Aung, Viroon Kamchoom‬, Zaw Zaw Aye

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringGeologyDiaphragm (acoustics)ExcavationEngineering

Abstract

fetched live from OpenAlex

Deep excavation in Bangkok clay layers involving diaphragm walls can cause ground movement, potentially affecting nearby structures. Understanding the magnitude and profile of this movement is crucial for assessing its impact on adjacent buildings. This study examines factors influencing the lateral displacement of rigid diaphragm walls in Bangkok’s deep excavations, including construction methods, excavation duration, depth ratios, soft clay depth, and system stiffness. The research data were collected from 230 dataset of lateral movement in diaphragm walls with a thickness ranging from 0.60 to 1.50 m. These walls had toe depths between 14 and 65 m, across various excavation depths ( H e ) from 6 to 35 m. Maximum lateral wall displacements ranged from 0.10% H e to 0.27% H e for the top-down method, and from 0.20% H e to 0.50% H e for the bottom-up method. If the system stiffness is sufficient, variations in wall thickness and construction method have minimal impact on wall deflection. However, with the bottom-up method and 1.0 m thick walls, long excavation times can lead to displacements up to 0.60% H e . This is mainly due to consolidation and creep in the clay beneath the area where the base slab construction is delayed.

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 score0.107
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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

Citations14
Published2024
Admission routes1
Has abstractyes

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