Engineered Bamboo for Sustainable Construction: A Systematic Review of Characterization Methods
Bibliographic record
Abstract
Engineered bamboo has been considered a viable replacement for traditional wood and steel for structural and architectural purposes due to its renewable nature, high strength, and compatibility with different processing techniques. This systematic review analyzed the literature on the mechanical properties and processing techniques of engineered bamboo products, which include bamboo scrimber and laminated bamboo. The literature included in this systematic review was extracted from the Engineering Village platform. The studies retrieved from this platform were filtered to only have been published in top journals (Q1/Q2) related to engineering materials, materials science, and the construction industry. Using this methodology, from the initial 191 identified records, 51 studies that were the most relevant were chosen. The review revealed that bamboo scrimber has better performance for specific mechanical properties, which include its compressive, tensile, and bending strength. Laminated products had higher variability, which was often caused by the type of adhesive, orientation, and quality of adhesion. This study also identified the details of manufacturing processes, such as the adhesive systems, pre-treatment methods, and pressing conditions used. Moreover, the literature exhibited considerable inconsistencies in testing standards, reporting practices, and long-term durability evaluations. This review highlights these challenges and provides recommendations for future research to resolve these issues.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".