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Record W4221120846 · doi:10.1061/9780784484029.044

Performance of Station Excavations for LA Metro K (Crenshaw/LAX) Line

2022· article· en· W4221120846 on OpenAlexaff
Charbel Beaino, Youssef M. A. Hashash, Timothy Bernard, Abby Hutter, Maksymilian Jasiak, Jack Lawrence, Wendy Patricia, Michael Pearce, Anne Lemnitzer, Lisa Star, Namasivayam Sathialingam, Edward J. Cording, Thomas D. O’Rourke, Androush Danielians

Bibliographic record

VenueGeo-Congress 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsExcavationGeotechnical engineeringSettlement (finance)Deflection (physics)AlluviumStiffnessMetro stationGeologyEngineeringStructural engineeringComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

This paper presents the effects of excavations in Los Angeles on the surrounding ground surface. Three large excavations with varied support systems for stations constructed as part of the K (Crenshaw/LAX) Line Transit Project provide an opportunity to acquire data through an extensive geotechnical investigation and field monitoring program to further our understanding of soil-structure interaction and excavation-induced ground deformations. The ground conditions encountered on site include alluvial deposits of silts, clays, and sands. The data acquired illustrates that ground displacement and wall deflection largely depend on the stiffness of the excavation support system. Support systems with relatively high stiffness are able to successfully limit surface settlement behind excavations. The data shows that heaving caused by unloading governs the soil response given the relatively small lateral support wall deformations. Current empirical models are unable to capture this ground behavior. An update of these empirical relations is needed to represent this behavior in support of the design of future excavations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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