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Record W7161999648 · doi:10.82308/5750

Dynamic post-elastic response of transmission towers

2009· dissertation· en· W7161999648 on OpenAlexaboutno aff
Xiao Hong Zhang

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTransmission towerTowerTransmission lineElectric power transmissionFinite element methodNonlinear systemStiffnessOverhead lineConductor

Abstract

fetched live from OpenAlex

Collapse of transmission towers can occur due to accidental loads such as conductor breakages, failures of insulators or other components, either under every day conditions (components with marginal strengths) or under extreme conditions such as ice storms, thunderstorms, tornadoes, fires, explosions, heavy mass impacts, etc. Furthermore, the trigger of one tower collapse may cause a catastrophic cascading failure of the whole transmission line section as was observed in the 1998 ice storm in Quebec, Canada. Knowledge of the post-elastic capacity of towers is necessary to mitigate the risk of cascading failures in overhead lines. The thesis presents a detailed study of the post-elastic response of latticed towers combining advanced (highly nonlinear) finite element analysis and full-scale dynamic testing of four tower section prototypes. The lattice towers are modeled with special three-node beam elements that include nonlinear material constitutive models for post-elastic response and the geometric stiffness matrices for elements are progressively updated to account for the second order effects. The numerical models also include the effects of connection eccentricities between diagonal members and the main leg members. The numerical models have been used to plan the physical tests and for re-analysis of the models with the experimental loads as measured during the physical tests. Four full-scale transmission tower model sections were built and tested under different load scenarios to verify the results from the numerical analysis. The salient conclusions of the research as follows: The research demonstrates that it is possible to use post-elastic analysis to accurately predict the reserve strength of bolted lattice towers provided connection eccentricities are properly modeled at peaks or cross arms loading points and in diagonals connected only on one leg. Both the numerical model and experimental results indicate significant post-elastic reserve strength of the tower section. In the tower prototypes tested in this research, the post-elastic reserve strength was 1.22 for flexure-torsion (i.e. tower under longitudinal loading) governed by diagonals, and 1.37 for bending (i.e. tower in transverse loading) governed by inelastic buckling of the main legs. Diagonal members affect the failure modes of transmission towers and their connection design may be a weak link in the development of their post-elastic capacity. Diagonal members connected on one leg only are subject to biaxial bending, they cannot develop the full strength of their cross section since the unconnected leg takes much less stress on its entire length. Accurate pushover post-elastic analysis is an essential design tool to ensure that the tower capacity is adequate and that failure modes are safe, i.e. not leading to progressive collapse. With appropriate training, such analysis is feasible in a design office. Observations from physical tests, confirmed by numerical simulations, suggest that failure modes under pushover static and dynamic pulse loading are similar. The ultimate loads sustained by the prototypes in the dynamic tests are higher than their static counterparts (162kN vs. 126kN in bending, and 57kN vs. 51.2kN in flexure-torsion); this can be explained by the strain rate effects, which were particularly large in the bending test due to a mass dropping height of 6 m.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.837

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 head0.003
GPT teacher head0.221
Teacher spread0.218 · 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
Published2009
Admission routes1
Has abstractyes

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