An Inelastic Model for Analyzing Intermediately Slender Engineered Bamboo/Wood Columns Subjected to Biaxial Bending and Compression
Bibliographic record
Abstract
The engineered bamboo/wood composites (EBWCs) studied in this work included solid wood, wood-based composites, and bamboo-based composites. The basic characteristic of these products is that they have similar stress-strain relationships in the parallel to the grain direction because of their similar microscopic structures. The asymmetric stress-strain relationship in tension and compression presents a great challenge for the inelastic analysis of intermediately slender EBWC columns. In this study, a novel model was developed for the inelastic analysis of biaxially loaded intermediately slender EBWC columns with rectangular cross sections. The model provides a step by step method to evaluate the nonlinear responses and load-carrying capacities of these columns. Experiments on parallel strand bamboo columns loaded with biaxial eccentric loads were conducted to validate the model. Good agreement between the experimental and predicted results was achieved. The innovative elements of the model were the asymmetric properties of EBWCs in tension and compression, and its simplicity, which lends itself to implementation in engineering design calculations. The present work is an extension of a previous study by Huang et al. (2015a), and its objective was to develop an innovative inelastic analysis model evaluating biaxially loaded intermediately slender EBWC columns with rectangular cross sections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".