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Record W1832680492 · doi:10.1139/cgj-2015-0158

Reliability-based geotechnical design in 2014 Canadian Highway Bridge Design Code

2015· article· en· W1832680492 on OpenAlexafffundvenueabout
Gordon A. Fenton, Farzaneh Naghibi, David Dundas, Richard J. Bathurst, D. V. Griffiths

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsRoyal Military College of CanadaMinistry of Transportation of OntarioDalhousie University
FundersMinistère des Transports
KeywordsBridge (graph theory)EngineeringCivil engineeringCode (set theory)Geotechnical investigationGeotechnical engineeringConstruction engineeringForensic engineeringStructural engineeringComputer science

Abstract

fetched live from OpenAlex

Canada has two national civil codes of practice that include geotechnical design provisions: the National Building Code of Canada and the Canadian Highway Bridge Design Code. For structural designs, both of these codes have been employing a load and resistance factor format embedded within a limit states design framework since the mid-1970s. Unfortunately, limit states design in geotechnical engineering has been lagging well behind that in structural engineering for the simple fact that the ground is by far the most variable (and hence uncertain) of engineering materials. Although the first implementation of a geotechnical limit states design code appeared in Denmark in 1956, it was not until 1979 that the concept began to appear in Canadian design codes, i.e., in the Ontario Highway Bridge Design Code, which later became the Canadian Highway Bridge Design Code (CHBDC). The geotechnical design provisions in the CHBDC have evolved significantly since their inception in 1979. This paper describes the latest advances appearing in the CHBDC along with the steps taken to calibrate its recent geotechnical resistance and consequence factors.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.003

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 head0.026
GPT teacher head0.216
Teacher spread0.189 · 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 designNot applicable
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

Citations143
Published2015
Admission routes4
Has abstractyes

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