A SIMPLE METHOD FOR A RELIABLE MODELLING OF THE NONLINEAR BEHAVIOUR OF BOLTED CONNECTIONS IN STEEL LATTICE TOWERS
Bibliographic record
Abstract
The behaviour of bolted connections in steel lattice transmission line towers affects their load-bearing capacity and failure mode. Bolted connections are commonly modelled as pinned or fixed joints, but their behaviour lies between these two extremes and evolves in a nonlinear manner. Accordingly, an accurate finite element modelling of the structural response of complete steel lattice towers requires the consideration of various nonlinear phenomena involved in bolted connexions, such as bolt slippage. In this study, a practical method is proposed for the modelling of the nonlinear response of steel lattice tower connections involving one or multiple bolts. First, the local load-deformation behaviour of single-bolt lap connections is evaluated analytically depending on various geometric and material parameters and construction details. Then, the predicted nonlinear behaviour for a given configuration serves as an input to a 2D/3D numerical model of the entire assembly of plates in which the bolted joints are represented as discrete elements. For comparison purposes, an extensive experimental study comprising forty-four tests were conducted on steel plates assembled with one or two bolts. This approach is also extended to simulate the behaviour of assemblies including four bolts and the obtained results are checked against experimental datasets from the literature. The obtained results show that the proposed method can predict accurately the response of a variety of multi-bolt connections. A potential application of the strategy developed in this paper could be in the numerical modelling of full-scale steel lattice towers, particularly for a reliable estimation of the displacements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".