Pulling tests for tree support systems: evaluating strength on artificial balled and burlapped trees
Bibliographic record
Abstract
Mature trees are crucial in urban landscapes, delivering substantial ecological, aesthetic, and microclimatic benefits. Balled and burlapped (B&B) trees are widely used to accelerate canopy establishment, yet their small root balls and limited structural roots often result in poor anchorage, particularly under wind loading. This study evaluated four support methods through mechanical pulling tests and finite element wind force simulations: timber pole support, cable anchoring, steel support, and underground steel anchoring. The results showed statistically significant differences in performance across systems. Steel supports exhibited the highest pulling force resistance (12.53 kN), while underground steel anchors generated the greatest bending moment (16.16 kNm). Wind simulations confirmed that steel supports withstood the highest threshold wind speeds up to 77.52 km hr−1, while unsupported trees failed at just 27.72 km hr−1. These findings underscore the importance of support selection tailored to environmental exposure. Steel-based systems are recommended for storm-prone or exposed urban sites due to the significantly longer service life of steel supports compared to timber; they are more suitable for long-term maintenance in exposed areas where they face environmental corrosion. Conversely, lighter support systems may suffice in sheltered zones. The short service life of timber supports, especially in tropical climates, necessitates frequent maintenance or replacement, which can be costly in the long run. This research provides a biomechanical framework for selecting context-appropriate support systems, enhancing transplanted trees’ long-term stability and survival in tropical urban forestry.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".