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Multiple Resistance Factor Design for Shallow Transmission Line Structure Foundations

2003· article· en· W2129955653 on OpenAlexfundno aff
Kok‐Kwang Phoon, Fred H. Kulhawy, Mircea Grigoriu

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersMinistère des TransportsVanderbilt University
KeywordsServiceability (structure)Resistance FactorsLimit state designParametric statisticsReliability (semiconductor)Reliability engineeringEngineeringStructural engineeringTransmission lineSafety factorElectric power transmissionGeotechnical engineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents the development of simplified reliability-based design (RBD) equations that are suitable for spread foundations subjected to uplift. Emphasis is placed on the loading and foundation characteristics relevant to the electric utility industry. A general reliability calibration procedure is used to derive robust resistance/deformation factors for load and resistance factor design (LRFD) and multiple resistance factor design (MRFD) formats. Two target reliability indices of 3.2 and 2.6 are proposed based on an extensive study of existing designs for ultimate and serviceability limit state, respectively. The main advantage of using these RBD factors is that a known level of reliability can be consistently achieved over a wide range of design conditions. Simple design calculations using the MRFD format are shown to demonstrate their ability to account for parametric uncertainties in a rational manner.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.193
Teacher spread0.184 · 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 designBench or experimental
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

Citations123
Published2003
Admission routes1
Has abstractyes

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