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Record W4405120961 · doi:10.1680/jgeen.24.00258

A methodology for improved predictions of surface ground movements around shafts

2024· article· en· W4405120961 on OpenAlexaff
David M.G. Taborda, António M. G. Pedro, Stuart Hardy

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Geotechnical Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsBenchmark (surveying)Context (archaeology)Human settlementExcavationSet (abstract data type)Computer scienceGround movementField (mathematics)Empirical researchSurface (topology)Civil engineeringGeologyMarine engineeringStructural engineeringEngineeringGeotechnical engineeringGeodesyMathematicsGeometryStatistics

Abstract

fetched live from OpenAlex

Shafts are typically employed in urban environments to provide access or ventilation to underground structures such as stations, railways or highways. The choice of design is determined, among other things, by the need to control settlements at the surface, often estimated during early design stages using empirical expressions. These have been shown to have limited accuracy, failing to account appropriately for the effect of shaft diameter on the ground movements associated with shaft excavation. This paper reviews empirical expressions available in the literature in the context of a large database of settlements induced by shaft excavation in London. A comprehensive set of detailed numerical analyses is performed to enable the development of a new set of expressions capable of predicting accurately the computed vertical and horizontal ground movements at the surface. The new expressions are shown to provide better predictions of the observed field data than predictive expressions available in the literature, establishing a new benchmark against which future proposals can be assessed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Civil Engineers - Geotechnical EngineeringSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207