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Record W3000316576 · doi:10.1061/9780784481837.011

Effective Length Factor of Leg Member in Latticed Steel Tower

2018· article· en· W3000316576 on OpenAlexaff
Ming Lu, Miao Hao, Debalay Chakrabarti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicStructural Load-Bearing Analysis
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsTowerStructural engineeringFactor (programming language)EngineeringComputer science

Abstract

fetched live from OpenAlex

Latticed steel towers have been widely used to support overhead transmission lines worldwide. In a tower, leg members are intended to carry the major loads. It is a common practice to design a leg member based on the assumption that its effective length factor is unity (1.0). While this practice has been working well over time, no report is available in the literature to validate this assumption. In this paper, a nonlinear buckling analysis approach is first proposed by using a geometrically nonlinear finite element (FE) code in conjunction with the concept of trigger load. The proposed approach is able to re-produce the ASCE-10 column curve very nicely, proving its practical usefulness. This approach is then used to rigorously examine the unity effective length factor assumption for the leg members. For this purpose, leg members are modeled as a continuous beam-column with multiple linear elastic springs representing the supports of the associated bracing members. As a result, it is found that the unity assumption is valid only if a leg member is adequately supported on its both ends, and would be risky otherwise. Accordingly, a simple, semi-empirical equation is derived, based on the numerical parametric study, to correlate a leg member’s buckling load with the support stiffness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.237
Teacher spread0.228 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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