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Record W4396905805 · doi:10.1139/cgj-2023-0535

Design stability charts for construction procedure of basement walls using staged bermed excavation: a parametric study

2024· article· en· W4396905805 on OpenAlexvenueno aff
Miguel Cano, José Luis Pastor, Adrián Riquelme, Roberto Tomás

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersConselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat ValencianaMinisterio de Educación, Cultura y Deporte
KeywordsExcavationGeotechnical engineeringGeologyParametric statisticsBasementStability (learning theory)EngineeringStructural engineeringCivil engineeringComputer scienceMathematics

Abstract

fetched live from OpenAlex

The construction of basement walls using discontinuous staged berms is based on excavating the central zone of a lot and leaving a lateral berm—which is then removed in phases with unexcavated sections (buttresses) remaining until a concrete wall is completed in the excavated areas. It is a commonly used technique in many nations, but its use is unsupported by regulations or scientific studies. This paper addresses the need for a analysis of this technique and makes a study of the geotechnical parameters of the subsoils where it is applied, as well as the commonly used practices. The research involved over 4000 finite element method calculations integrating geotechnical parameters with construction geometry. The results have enabled the preparation of four stability design charts based on linear polynomial surface adjustment for two project scenarios: with and without surcharge load. This paper proposes the use of these stability design charts for staged bermed excavations in a broad spectrum of soil types and the incorporation of a designer-defined safety level to ensure temporary stability. Additional charts are provided to assess the safety factor of projects once the geometries and geotechnical parameters of the subsoil are known.

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)0.001
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 score0.729
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.032
GPT teacher head0.239
Teacher spread0.208 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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