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Record W4396547664 · doi:10.1080/19386362.2024.2344408

Calibration of resistance factors for reliability-based design of driven steel piles considering separate factors for toe and shaft resistances

2023· article· en· W4396547664 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Geotechnical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPileFoundation (evidence)Reliability (semiconductor)Structural engineeringGeotechnical engineeringEngineeringResistance FactorsField (mathematics)Mathematics

Abstract

fetched live from OpenAlex

The LRFD method employs the geotechnical resistance factor (GRF) to design pile foundations. Different resistance mechanisms at the end and side of the pile lead to the requirement of separate GRF values for the shaft and toe capacities. The main aim of this study is to establish regionally separate GRFs for the toe and shaft resistances of pile foundations using different reliability analysis methods. A comprehensive database was gathered from field tests conducted in Alberta, Canada, involved 623 Pile Driving Analyzer (PDA) tests and 28 static load tests. Two criteria called ‘70% rule’ and ‘R ratio’ were used to evaluate the results. Due to certain limitations in the definition of the ‘70% rule’, the ‘R ratio’ method yielded more realistic results. The results indicated that employing separate GRFs for shaft and toe resistances can lead to a significant increase in the factored pile resistance when compared to the values suggested by the Canadian Foundation Engineering Manual (CEFM) in the case of redundant piles.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.689
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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