MétaCan
Menu
Back to cohort

Assessment of Current Load Factors for Use in Geotechnical Load and Resistance Factor Design

2003· article· en· W2117516527 on OpenAlexaboutno aff
B. Scott, B. J. Kim, Rodrigo Salgado

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
FundersIndiana Department of TransportationPurdue University
KeywordsLoad factorResistance FactorsStructural engineeringFoundation (evidence)EngineeringGeotechnical engineeringReliability (semiconductor)Structural loadCurrent (fluid)Load resistanceMoment (physics)Reliability engineeringCivil engineeringGeography

Abstract

fetched live from OpenAlex

Load and resistance factor design (LRFD) is the standard structural design practice. In order for foundation design to be consistent with current structural design practice, the use of the same loads, load factors, and load combinations would be required. In this paper, we review the load factors presented in various LRFD codes from the United States, Canada, and Europe. A simple first-order second-moment (FOSM) reliability analysis is presented to determine appropriate ranges for the values of the load factors. These values are compared with those proposed in the codes. The comparisons between the analysis and the codes show that the values of load factors given in the codes generally fall within ranges consistent with the results of the FOSM analysis. However, it would be desirable for the successful development and adoption of the geotechnical component of LRFD codes to have uniformity of load-factor values across different codes for the loads that are common for virtually all civil structures.

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)0.007
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.228
Teacher spread0.210 · 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 designObservational
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

Citations46
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geotechnical and Geoenvironmental EngineeringSame topicGeotechnical Engineering and AnalysisFrench-language works237,207