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Record W2150954065 · doi:10.1139/t10-005

A factored strength approach for the limit states design of geotechnical structures

2010· article· en· W2150954065 on OpenAlexvenueno aff
Luigi Callisto

Bibliographic record

VenueCanadian Geotechnical Journal · 2010
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFactoringReliability (semiconductor)Geotechnical engineeringLimit (mathematics)Nonlinear systemMeasure (data warehouse)Limit analysisStructural engineeringEngineeringMathematicsComputer scienceData miningFinite element methodMathematical analysisPhysics

Abstract

fetched live from OpenAlex

This paper discusses the implications of an approach to the limit states design of geotechnical structures in which the soil strength parameters are factored, as is the one contained in the European construction code. It is shown that a factored strength approach has very different implications for the two customary types of geotechnical analyses; namely, the study of plastic mechanisms and interaction analyses. The relationship between partial safety factors and the reliability of a system is highlighted first, with the aid of approximate reliability calculations relative to two example problems. It is then demonstrated that the method of factoring the soil strength may be regarded as an effective way to assess the distance of a structure from the activation of a plastic mechanism, and to relate this distance to a conventional measure of its reliability. Using the same examples, it is argued that the procedure of factoring the soil strength parameters is not appropriate for the evaluation of the internal forces in structural elements: most of the soil–structure interaction analyses are nonlinear and, at least partly, empirically based. The insertion of unrealistic parameters, such as the factored strength properties, in a complex calculation may produce ambiguous and uncontrollable results.

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: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.002
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 head0.012
GPT teacher head0.198
Teacher spread0.186 · 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
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

Citations13
Published2010
Admission routes1
Has abstractyes

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