Buckling behaviour of trees under self-weight loading
Bibliographic record
Abstract
Abstract Understanding tree stability under self-weight and applied loads from wind and snow is important when developing management strategies to reduce the risk of damage from these abiotic agents. In this paper, linear buckling analysis was conducted using the finite element method to identify the instability modes of a tree structure under a specified set of loads. A non-prismatic elastic circular column of height H was analysed, taking self-weight into account. Various scenarios were considered: column taper, base rigidity, radial and longitudinal stiffness, ellipticity and crown weight. The effect on the critical buckling height was assessed in each case. Validation against closed form solutions of benchmark problems was conducted satisfactorily. The results indicate that column taper, base rigidity and the stiffness/density ratio are particularly important for this problem. Further comparison was made using data from a 15-year old Pinus radiata stand in New Zealand, which contained both buckled and non-buckled trees. While the model predicted factors of safety against buckling that were close to unity, it was unable to differentiate between buckled and non-buckled trees. Further investigation is needed to examine the reasons why this occurred. Despite this, the current study provides an in-depth numerical investigation, which has aided our understanding of the effects that material properties, geometric properties and boundary conditions have on buckling phenomenon in trees.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".