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Record W2895813998 · doi:10.1139/cgj-2018-0433

Calibration of resistance factors for geotechnical seismic design

2018· article· en· W2895813998 on OpenAlexaffvenueabout
Farzaneh Naghibi, Gordon A. Fenton

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeotechnical engineeringSeismic analysisEarthquake resistanceResistance FactorsEngineeringSeismic resistanceReturn periodEarthquake shaking tableBridge (graph theory)Seismic loadingEarthquake resistant structuresCivil engineeringStructural engineeringForensic engineering

Abstract

fetched live from OpenAlex

The next edition of the Canadian Highway Bridge Design Code will contain a table of geotechnical resistance factors to be used for seismic design. This paper will estimate the geotechnical resistance factors for shallow foundations required to achieve various target maximum acceptable failure probabilities, which in turn may depend on the assumed design earthquake return period. The investigation will include consideration of design lifetime, uncertainty in the magnitude of the maximum lifetime earthquake event, and the uncertainty in ground properties. The results suggest resistance factors that are lower than commonly used at the moment in Canada and that the failure probability is not greatly dependent on the return period of the design earthquake. The paper will present recommendations on geotechnical resistance factors for seismic design that can be used to guide and calibrate future editions of civil design codes in Canada.

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)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.390
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.015
GPT teacher head0.206
Teacher spread0.192 · 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 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
Published2018
Admission routes3
Has abstractyes

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