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Record W194451476

Assessment of Partial Safety Factors for Limit States Design of Foundations

2004· article· en· W194451476 on OpenAlexaboutno aff
Kim Bum-Joo

Bibliographic record

VenueJournal of the Korean Geotechnical Society · 2004
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsResistance FactorsFoundation (evidence)Bearing capacityReliability (semiconductor)Geotechnical engineeringStructural engineeringLoad factorEngineeringReliability engineeringLimit (mathematics)Moment (physics)Safety factorLoad bearingFactor of safetyCivil engineeringMathematicsGeography
DOInot available

Abstract

fetched live from OpenAlex

While limit states design (LSD) is currently the standard structural design practice, it is relatively new in the geotechnical design. Adoption of LSD far geotechnical design is an international trend. In the present study, various LSD codes from the United States, Canada, and Europe were reviewed. A simple first-order-second-moment (FOSM) reliability analysis was performed to determine theoretically the ranges of load and resistance factor values for representative loads and foundation bearing capacity, respectively. In order for foundation design to be consistent with current structural design practice, it would be desirable to use the same loads, load factors and load combinations. The values of load factor, obtained from the FOSM analysis, were found to be generally consistent with those given in the codes, whereas the values of resistance factor indicated overall lower ranges due to high values of coefficient of variation used in the analysis. Since the degree of uncertainties included in bearing capacity of foundations varies with the methods used to estimate the bearing capacity, different values of resistance factor should be used fur different methods. For the purpose, continuous efforts are needed to be made first to accurately identify and quantify the uncertainties in the methods.

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)0.000
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 score0.907
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.018
GPT teacher head0.256
Teacher spread0.237 · 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.

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

Citations0
Published2004
Admission routes1
Has abstractyes

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Same venueJournal of the Korean Geotechnical SocietySame topicGeotechnical Engineering and AnalysisFrench-language works237,207