Modelling strategy to implement the local buckling and the non-linear behaviour of bolted connections in the analysis of steel lattice transmission towers
Bibliographic record
Abstract
Abstract : The development of finite element method with the aid of powerful software has made it possible to analyze and design complicated civil engineering structures with more accuracy and reliability. Among these, electricity transmission line structures play an important role in our economy. Current practice in transmission line steel lattice tower design has many simplifying assumptions and cannot cover the real behaviour of towers under various loading conditions. Meanwhile, to qualify the design, a full-scale tower test has to be performed which is expensive and time consuming. Advanced numerical models have been developed in the past to simulate with more precision the behaviour of lattice towers. These models can limit and optimize the use of full-scale tests. However, some aspects of the non-linear structural behaviour of towers is still not efficiently taken into account in those advanced numerical methods. In this research, an advanced numerical modelling and analysis procedure using the finite element method and predicting the behaviour up to failure of lattice towers made of steel angles is proposed. Two main subjects for non-linear modelling of towers are considered. Firstly, the local buckling failure of members using 1D beam elements is addressed by providing a new method to modify the material behaviour of the member. Secondly, a method is presented to predict the behaviour of various configurations of bolted steel member connections. The predicted behaviour then can be applied as a non-linear spring element to model the connections in the full tower. Using these methods less modelling effort, complication and time will be needed for tower projects and full-scale modelling. In addition, more accurate results are expected as we cover more non-linear aspects of the structure behaviour.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".