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Record W3003234817 · doi:10.1139/cgj-2019-0041

Charts for the mining-induced deflection of buildings

2020· article· en· W3003234817 on OpenAlexvenueno aff
Andrea Franza, Olivier Deck, Matthew J. DeJong

Bibliographic record

VenueCanadian Geotechnical Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDeflection (physics)Structural engineeringGround movementStiffnessCurvatureGeotechnical engineeringParametric statisticsGround subsidenceEngineeringDimensionless quantityMathematicsGeometryMechanicsPhysics

Abstract

fetched live from OpenAlex

This study investigates the flexural deformations of buildings induced by mining-related subsidence. A two-stage solution (previously applied to the tunnelling problem) is used. Buildings are modelled as a beam founded on an elastic continuum that is subjected to ground subsidence. A tensionless soil–structure interface is employed. The effects of both building stiffness and self-weight on the building deflection are considered. The proposed formulation is compared with analytical solutions and empirical envelopes from previous research. A parametric study is conducted for both perfect soil–foundation bond and gap formation to relate deflection ratio modification factors and the limit radius of the greenfield settlement curve (associated with the gap formation) to relative structure–soil stiffness, building weight, foundation shape, and greenfield ground curvature (hogging or sagging) by means of dimensionless groups. Finally, a simple case study is used to demonstrate the proposed procedure. The given framework is more comprehensive than design charts provided by previous mining-related works and can be used for rapid preliminary risk assessment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.207
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations4
Published2020
Admission routes1
Has abstractyes

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